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What does a successful military-to-civilian transition look like? How do we know if a veteran is sinking, treading water, or swimming? Two recent studies by the Penn State University Clearinghouse for Military Family Readiness sought to answer to those questions and more while determining how and when is the right time to step in to help a veteran in need.

The research analyzed The Veterans Metrics Initiative data (TVMI). This longitudinal study surveyed 9566 men and women who left active duty in 2016 over 3 years, answering questions about deployment histories, adverse childhood experiences (ACEs) and exposure to combat. They also reported whether they had symptoms related to anxiety and depression. 

The TVMI study found that ACEs predicted poor outcomes early on and when combined with warfare experience dramatically increased the likelihood of mental health issues, including posttraumatic stress disorder (PTSD), anxiety, and depression; moral injury impacted adjustment to civilian life (the degree varied by gender); and, many veterans have a “growth outlook” as a result of a trauma or crisis they experienced.

The TVMI study found that almost all veterans use transition resources in the first 2 years after military separation. Beyond that, however, those in high-risk categories (eg, PTSD and cumulative trauma experiences) need continued support. This may come in the form of a universal screener and linking it to a navigation infrastructure (eg, AmericaServes), “thereby identifying risk factors early and providing targeted supports, interventions, and components.”

Veterans often face a series of simultaneous challenges as they return to civilian life. Among them include getting used to family and friends again, finding jobs, losing their military identity, structure, and perhaps leaving military friends behind. In addition, veterans are likely dealing with physical and mental health challenges, which can significantly influence how well they readjust to civilian life and lead to inconsistency experiences for each individual.

A 2019 survey from the Pew Research Center found about 40% of veterans who suffered from PTSD said they frequently had difficulty dealing with the lack of structure in civilian life, compared with 5% of those who do not have PTSD. Another survey cited a large majority (78%) of pre-9/11 veterans said their readjustment was very or somewhat easy. However, 26% said adjusting to civilian life was difficult.

In 2011, 4 variables were identified that predicted easy civilian life re-entry: being an officer; having a consistently clear understanding of the missions while in the service; being a college graduate; and, for post-9/11 veterans, attending religious services frequently. Six variables were associated with a diminished probability of an easy transition: having had a traumatic experience; being seriously injured; serving in the post-9/11 era; serving in a combat zone; serving with someone who was killed or injured; and, for post-9/11 veterans, being married while in the service.

The probabilities of an easy re-entry dropped from 82% for those who did not experience a traumatic event to 56% for those who did—the largest change noted in the 2011 study.

The second Penn State study evaluated a model framework with a lifespan development perspective. The study surveyed veterans on their self-reported satisfaction or symptoms in 7 domains of well-being: employment, education, financial, legal problems, social, physical health, and mental health. Within 3 months of separation , 41% of respondents fell into the “problematic” category for the mental health domain. However, by 30 to 33 months postseparation, this proportion dropped to 34%. During the same period, the proportion of veterans in the at risk category increased from 28% at Wave 1 to 37% at Wave 6. About 30% of veterans fell into the successful category for symptoms across the 3 examined waves. Almost 60% were in the successful category across the 3 time points.

Both Penn State studies emphasize the importance of viewing veterans as individuals on their own timelines.

“These findings underscore that the transition to civilian life is not a single moment, but a process influenced by experiences across the life span,” said Mary M. Mitchell, research professor at the Clearinghouse and lead author on the predictors study. “By following veterans over 3 years, we were able to see how patterns emerge that would be invisible in a one-time survey.”

Current conceptualization “assumes that there are commonalities across veterans when evaluating the success of the transition to civilian life,” according to the authors of the framework study. “However, each veteran likely has his or her view of what a successful transition constitutes, and he or she may weigh domains differently when considering his or her own transition.”

The research highlights the need to find ways to encourage veterans to seek help—and not just in the first year, which is often the most stressful. The Pew Research Center survey identified a “significant break from the past,” in that nearly 70% of post-9/11 veterans said their superiors made them feel comfortable about seeking help with emotional issues resulting from their military service. 

However, ≤ 8% veterans in the TVMI study used any health programs, even when they screened positive for mental health problems. Veterans who did use counseling services, however, improved their depression symptoms. Engaging veterans at various time points could help keep mental health problems from worsening during—and beyond—the transition.

What does a successful military-to-civilian transition look like? How do we know if a veteran is sinking, treading water, or swimming? Two recent studies by the Penn State University Clearinghouse for Military Family Readiness sought to answer to those questions and more while determining how and when is the right time to step in to help a veteran in need.

The research analyzed The Veterans Metrics Initiative data (TVMI). This longitudinal study surveyed 9566 men and women who left active duty in 2016 over 3 years, answering questions about deployment histories, adverse childhood experiences (ACEs) and exposure to combat. They also reported whether they had symptoms related to anxiety and depression. 

The TVMI study found that ACEs predicted poor outcomes early on and when combined with warfare experience dramatically increased the likelihood of mental health issues, including posttraumatic stress disorder (PTSD), anxiety, and depression; moral injury impacted adjustment to civilian life (the degree varied by gender); and, many veterans have a “growth outlook” as a result of a trauma or crisis they experienced.

The TVMI study found that almost all veterans use transition resources in the first 2 years after military separation. Beyond that, however, those in high-risk categories (eg, PTSD and cumulative trauma experiences) need continued support. This may come in the form of a universal screener and linking it to a navigation infrastructure (eg, AmericaServes), “thereby identifying risk factors early and providing targeted supports, interventions, and components.”

Veterans often face a series of simultaneous challenges as they return to civilian life. Among them include getting used to family and friends again, finding jobs, losing their military identity, structure, and perhaps leaving military friends behind. In addition, veterans are likely dealing with physical and mental health challenges, which can significantly influence how well they readjust to civilian life and lead to inconsistency experiences for each individual.

A 2019 survey from the Pew Research Center found about 40% of veterans who suffered from PTSD said they frequently had difficulty dealing with the lack of structure in civilian life, compared with 5% of those who do not have PTSD. Another survey cited a large majority (78%) of pre-9/11 veterans said their readjustment was very or somewhat easy. However, 26% said adjusting to civilian life was difficult.

In 2011, 4 variables were identified that predicted easy civilian life re-entry: being an officer; having a consistently clear understanding of the missions while in the service; being a college graduate; and, for post-9/11 veterans, attending religious services frequently. Six variables were associated with a diminished probability of an easy transition: having had a traumatic experience; being seriously injured; serving in the post-9/11 era; serving in a combat zone; serving with someone who was killed or injured; and, for post-9/11 veterans, being married while in the service.

The probabilities of an easy re-entry dropped from 82% for those who did not experience a traumatic event to 56% for those who did—the largest change noted in the 2011 study.

The second Penn State study evaluated a model framework with a lifespan development perspective. The study surveyed veterans on their self-reported satisfaction or symptoms in 7 domains of well-being: employment, education, financial, legal problems, social, physical health, and mental health. Within 3 months of separation , 41% of respondents fell into the “problematic” category for the mental health domain. However, by 30 to 33 months postseparation, this proportion dropped to 34%. During the same period, the proportion of veterans in the at risk category increased from 28% at Wave 1 to 37% at Wave 6. About 30% of veterans fell into the successful category for symptoms across the 3 examined waves. Almost 60% were in the successful category across the 3 time points.

Both Penn State studies emphasize the importance of viewing veterans as individuals on their own timelines.

“These findings underscore that the transition to civilian life is not a single moment, but a process influenced by experiences across the life span,” said Mary M. Mitchell, research professor at the Clearinghouse and lead author on the predictors study. “By following veterans over 3 years, we were able to see how patterns emerge that would be invisible in a one-time survey.”

Current conceptualization “assumes that there are commonalities across veterans when evaluating the success of the transition to civilian life,” according to the authors of the framework study. “However, each veteran likely has his or her view of what a successful transition constitutes, and he or she may weigh domains differently when considering his or her own transition.”

The research highlights the need to find ways to encourage veterans to seek help—and not just in the first year, which is often the most stressful. The Pew Research Center survey identified a “significant break from the past,” in that nearly 70% of post-9/11 veterans said their superiors made them feel comfortable about seeking help with emotional issues resulting from their military service. 

However, ≤ 8% veterans in the TVMI study used any health programs, even when they screened positive for mental health problems. Veterans who did use counseling services, however, improved their depression symptoms. Engaging veterans at various time points could help keep mental health problems from worsening during—and beyond—the transition.

What does a successful military-to-civilian transition look like? How do we know if a veteran is sinking, treading water, or swimming? Two recent studies by the Penn State University Clearinghouse for Military Family Readiness sought to answer to those questions and more while determining how and when is the right time to step in to help a veteran in need.

The research analyzed The Veterans Metrics Initiative data (TVMI). This longitudinal study surveyed 9566 men and women who left active duty in 2016 over 3 years, answering questions about deployment histories, adverse childhood experiences (ACEs) and exposure to combat. They also reported whether they had symptoms related to anxiety and depression. 

The TVMI study found that ACEs predicted poor outcomes early on and when combined with warfare experience dramatically increased the likelihood of mental health issues, including posttraumatic stress disorder (PTSD), anxiety, and depression; moral injury impacted adjustment to civilian life (the degree varied by gender); and, many veterans have a “growth outlook” as a result of a trauma or crisis they experienced.

The TVMI study found that almost all veterans use transition resources in the first 2 years after military separation. Beyond that, however, those in high-risk categories (eg, PTSD and cumulative trauma experiences) need continued support. This may come in the form of a universal screener and linking it to a navigation infrastructure (eg, AmericaServes), “thereby identifying risk factors early and providing targeted supports, interventions, and components.”

Veterans often face a series of simultaneous challenges as they return to civilian life. Among them include getting used to family and friends again, finding jobs, losing their military identity, structure, and perhaps leaving military friends behind. In addition, veterans are likely dealing with physical and mental health challenges, which can significantly influence how well they readjust to civilian life and lead to inconsistency experiences for each individual.

A 2019 survey from the Pew Research Center found about 40% of veterans who suffered from PTSD said they frequently had difficulty dealing with the lack of structure in civilian life, compared with 5% of those who do not have PTSD. Another survey cited a large majority (78%) of pre-9/11 veterans said their readjustment was very or somewhat easy. However, 26% said adjusting to civilian life was difficult.

In 2011, 4 variables were identified that predicted easy civilian life re-entry: being an officer; having a consistently clear understanding of the missions while in the service; being a college graduate; and, for post-9/11 veterans, attending religious services frequently. Six variables were associated with a diminished probability of an easy transition: having had a traumatic experience; being seriously injured; serving in the post-9/11 era; serving in a combat zone; serving with someone who was killed or injured; and, for post-9/11 veterans, being married while in the service.

The probabilities of an easy re-entry dropped from 82% for those who did not experience a traumatic event to 56% for those who did—the largest change noted in the 2011 study.

The second Penn State study evaluated a model framework with a lifespan development perspective. The study surveyed veterans on their self-reported satisfaction or symptoms in 7 domains of well-being: employment, education, financial, legal problems, social, physical health, and mental health. Within 3 months of separation , 41% of respondents fell into the “problematic” category for the mental health domain. However, by 30 to 33 months postseparation, this proportion dropped to 34%. During the same period, the proportion of veterans in the at risk category increased from 28% at Wave 1 to 37% at Wave 6. About 30% of veterans fell into the successful category for symptoms across the 3 examined waves. Almost 60% were in the successful category across the 3 time points.

Both Penn State studies emphasize the importance of viewing veterans as individuals on their own timelines.

“These findings underscore that the transition to civilian life is not a single moment, but a process influenced by experiences across the life span,” said Mary M. Mitchell, research professor at the Clearinghouse and lead author on the predictors study. “By following veterans over 3 years, we were able to see how patterns emerge that would be invisible in a one-time survey.”

Current conceptualization “assumes that there are commonalities across veterans when evaluating the success of the transition to civilian life,” according to the authors of the framework study. “However, each veteran likely has his or her view of what a successful transition constitutes, and he or she may weigh domains differently when considering his or her own transition.”

The research highlights the need to find ways to encourage veterans to seek help—and not just in the first year, which is often the most stressful. The Pew Research Center survey identified a “significant break from the past,” in that nearly 70% of post-9/11 veterans said their superiors made them feel comfortable about seeking help with emotional issues resulting from their military service. 

However, ≤ 8% veterans in the TVMI study used any health programs, even when they screened positive for mental health problems. Veterans who did use counseling services, however, improved their depression symptoms. Engaging veterans at various time points could help keep mental health problems from worsening during—and beyond—the transition.

TOPLINE:

Energy insecurity, the inability to meet household energy needs, was associated with more than twice the odds of having depression and anxiety symptoms than energy security in US adults, a new cross-sectional study showed.

METHODOLOGY:

• Using data from the US Census Bureau's online Household Pulse Survey, administered between 2022 and 2024, researchers conducted a cross-sectional study with a weighted population of > 187 million US adults (51% women; 64% White, 16% Hispanic, 10% Black, and 5% Asian). About a quarter of the population was in each of 4 age groups: 18-34 years, 35-49 years, 50-64 years, and ≥ 65 years.
• Three indicators of energy insecurity—inability to pay energy bills, maintaining unsafe/unhealthy home temperatures, and forgoing expenses on basic necessities to pay energy bills—were assessed individually and as a composite measure.
• Mental health was assessed using modified versions of the 2-item Patient Health Questionnaire for depression and the 2-item Generalized Anxiety Disorder scale for anxiety.
• The analysis was adjusted for other social determinants of health, including unemployment, housing instability, and food insecurity. Covariates included a wide range of factors, such as age, educational level, sex, and annual household income.

TAKEAWAY:

• In all, > 43% of the population reported having ≥ 1 form of energy security; around 22% reported being unable to pay energy bills, 22% maintained unsafe home temperatures, and nearly 34% forewent spending on basic necessities to pay energy bills.
• Individuals who gave up spending on basic necessities to pay energy bills had higher odds of anxiety (adjusted odds ratio [aOR], 1.79) and depression (aOR, 1.74) than those who did not.
• Adults with energy insecurity on the composite measure had higher odds for anxiety (aOR, 2.29) and depression (aOR, 2.31) than those with energy security.
• Food insecurity was also associated with poorer mental health, with higher odds for symptoms of depression (aOR, 2.05) and anxiety (aOR, 2.07).

IN PRACTICE:

"Despite its high prevalence, energy insecurity remains underrecognized in public health and policy intervention strategies," the investigators wrote.

"These findings suggest that energy insecurity is a widespread and important factor associated with mental health symptoms and may warrant consideration in efforts to reduce adverse mental health outcomes," they added.

SOURCE:

This study was led by Michelle Graf, PhD, Carter School of Public Policy, Georgia Institute of Technology, Atlanta. It was published online on October 27 in JAMA Network Open.

LIMITATIONS:

The cross-sectional nature of the data limited causal interference and increased the possibility of reverse causality. The questionnaire captured subjective interpretations of unsafe and unhealthy indoor temperatures, which may have varied among respondents. Additionally, the recall periods for energy insecurity and mental health outcomes were different.

DISCLOSURES:

The investigators reported no relevant conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

TOPLINE:

Energy insecurity, the inability to meet household energy needs, was associated with more than twice the odds of having depression and anxiety symptoms than energy security in US adults, a new cross-sectional study showed.

METHODOLOGY:

• Using data from the US Census Bureau's online Household Pulse Survey, administered between 2022 and 2024, researchers conducted a cross-sectional study with a weighted population of > 187 million US adults (51% women; 64% White, 16% Hispanic, 10% Black, and 5% Asian). About a quarter of the population was in each of 4 age groups: 18-34 years, 35-49 years, 50-64 years, and ≥ 65 years.
• Three indicators of energy insecurity—inability to pay energy bills, maintaining unsafe/unhealthy home temperatures, and forgoing expenses on basic necessities to pay energy bills—were assessed individually and as a composite measure.
• Mental health was assessed using modified versions of the 2-item Patient Health Questionnaire for depression and the 2-item Generalized Anxiety Disorder scale for anxiety.
• The analysis was adjusted for other social determinants of health, including unemployment, housing instability, and food insecurity. Covariates included a wide range of factors, such as age, educational level, sex, and annual household income.

TAKEAWAY:

• In all, > 43% of the population reported having ≥ 1 form of energy security; around 22% reported being unable to pay energy bills, 22% maintained unsafe home temperatures, and nearly 34% forewent spending on basic necessities to pay energy bills.
• Individuals who gave up spending on basic necessities to pay energy bills had higher odds of anxiety (adjusted odds ratio [aOR], 1.79) and depression (aOR, 1.74) than those who did not.
• Adults with energy insecurity on the composite measure had higher odds for anxiety (aOR, 2.29) and depression (aOR, 2.31) than those with energy security.
• Food insecurity was also associated with poorer mental health, with higher odds for symptoms of depression (aOR, 2.05) and anxiety (aOR, 2.07).

IN PRACTICE:

"Despite its high prevalence, energy insecurity remains underrecognized in public health and policy intervention strategies," the investigators wrote.

"These findings suggest that energy insecurity is a widespread and important factor associated with mental health symptoms and may warrant consideration in efforts to reduce adverse mental health outcomes," they added.

SOURCE:

This study was led by Michelle Graf, PhD, Carter School of Public Policy, Georgia Institute of Technology, Atlanta. It was published online on October 27 in JAMA Network Open.

LIMITATIONS:

The cross-sectional nature of the data limited causal interference and increased the possibility of reverse causality. The questionnaire captured subjective interpretations of unsafe and unhealthy indoor temperatures, which may have varied among respondents. Additionally, the recall periods for energy insecurity and mental health outcomes were different.

DISCLOSURES:

The investigators reported no relevant conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

TOPLINE:

Energy insecurity, the inability to meet household energy needs, was associated with more than twice the odds of having depression and anxiety symptoms than energy security in US adults, a new cross-sectional study showed.

METHODOLOGY:

• Using data from the US Census Bureau's online Household Pulse Survey, administered between 2022 and 2024, researchers conducted a cross-sectional study with a weighted population of > 187 million US adults (51% women; 64% White, 16% Hispanic, 10% Black, and 5% Asian). About a quarter of the population was in each of 4 age groups: 18-34 years, 35-49 years, 50-64 years, and ≥ 65 years.
• Three indicators of energy insecurity—inability to pay energy bills, maintaining unsafe/unhealthy home temperatures, and forgoing expenses on basic necessities to pay energy bills—were assessed individually and as a composite measure.
• Mental health was assessed using modified versions of the 2-item Patient Health Questionnaire for depression and the 2-item Generalized Anxiety Disorder scale for anxiety.
• The analysis was adjusted for other social determinants of health, including unemployment, housing instability, and food insecurity. Covariates included a wide range of factors, such as age, educational level, sex, and annual household income.

TAKEAWAY:

• In all, > 43% of the population reported having ≥ 1 form of energy security; around 22% reported being unable to pay energy bills, 22% maintained unsafe home temperatures, and nearly 34% forewent spending on basic necessities to pay energy bills.
• Individuals who gave up spending on basic necessities to pay energy bills had higher odds of anxiety (adjusted odds ratio [aOR], 1.79) and depression (aOR, 1.74) than those who did not.
• Adults with energy insecurity on the composite measure had higher odds for anxiety (aOR, 2.29) and depression (aOR, 2.31) than those with energy security.
• Food insecurity was also associated with poorer mental health, with higher odds for symptoms of depression (aOR, 2.05) and anxiety (aOR, 2.07).

IN PRACTICE:

"Despite its high prevalence, energy insecurity remains underrecognized in public health and policy intervention strategies," the investigators wrote.

"These findings suggest that energy insecurity is a widespread and important factor associated with mental health symptoms and may warrant consideration in efforts to reduce adverse mental health outcomes," they added.

SOURCE:

This study was led by Michelle Graf, PhD, Carter School of Public Policy, Georgia Institute of Technology, Atlanta. It was published online on October 27 in JAMA Network Open.

LIMITATIONS:

The cross-sectional nature of the data limited causal interference and increased the possibility of reverse causality. The questionnaire captured subjective interpretations of unsafe and unhealthy indoor temperatures, which may have varied among respondents. Additionally, the recall periods for energy insecurity and mental health outcomes were different.

DISCLOSURES:

The investigators reported no relevant conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

TOPLINE:

A comprehensive review of 92 studies found that 15% to 20% of lung cancers occurred among nonsmokers and were associated with environmental and germline risk factors. These cancers frequently harbored actionable genomic drivers, and targeted EGFR and ALK therapies produced significant diseasefree survival (DFS) and overall survival benefits.

METHODOLOGY:

• Lung cancer continues to be the leading cause of cancer death worldwide, causing about 1.8 million deaths in 2022, with smoking remaining the predominant risk factor. However, the incidence of lung cancer among nonsmokers (those who have smoked less than 100 cigarettes in their lifetime) is rising, varies by sex and geography, and is linked to environmental exposures and family history. The misperception that lung cancer is almost invariably caused by smoking may delay assessment and diagnosis.
• Researchers conducted a review of 92 studies on lung cancer in nonsmokers: 6 meta-analyses or systematic reviews, 16 randomized clinical trials, eight prospective cohort studies, seven retrospective cohort studies, three cross-sectional studies, four observational or case-control studies, 13 genomic studies, and 35 other studies.
• Overall, lung cancer among nonsmokers accounted for 15% to 20% of all lung cancer cases. Most lung cancers in nonsmokers were adenocarcinomas (60% to 80%), with a median age at diagnosis of 67 years in this group compared with 70 years in people with a history of smoking.
• Data analysis from three US hospital networks showed that the proportion of lung cancer among nonsmokers increased from 8.0% to 14.9% between 1990 and 2013. A pooled analysis of seven Finnish cohorts reported an absolute increase in lung cancer among nonsmokers from 6.9 per 100,000 person-years in 1972 to 12.9 per 100,000 person-years in 2015.
• The age-adjusted incidence rate of lung cancer in the US between 2000 and 2013 was 17.5 per 100,000 individuals among Asian female nonsmokers compared with 10.1 per 100,000 among non-Hispanic White female nonsmokers.

TAKEAWAY:

• Environmental and occupational risk factors were secondhand smoke, residential radon, outdoor and household air pollution (PM_2.5), asbestos and silica exposure, and prior thoracic radiotherapy. Having a first-degree relative with lung cancer increased the risk of developing lung cancer, and genome-wide association studies identified susceptibility loci associated with lung cancer risk in nonsmokers.
• Family history and inherited susceptibility increased lung cancer risk in never smokers (odds ratio [OR] for lung cancer in those with a first–degree relative, 1.51), and clonal hematopoiesis was also associated with higher risk (OR, 1.43). Importantly, tumors in nonsmokers were frequently driven by actionable somatic alterations (EGFR mutations, 40% to 60% in nonsmokers compared with 10% in smokers) and enrichment of ALK/ROS1/RET/ERBB2/NTRK/NRG1 fusions; 78% to 92% of adenocarcinomas in nonsmokers harbored actionable drivers (compared with 49.5% in ever smokers), and nonsmokers had a substantially lower tumor mutational burden (10–fold lower).
• Similar to individuals with a history of smoking, nonsmokers with lung cancer presented with cough, pain, dyspnea, or weight loss or had disease detected incidentally. Surgical resection remained the preferred treatment for anatomically resectable lung cancer (stages I-III) in medically eligible patients, with follow-up CT screening recommended every 6 months for 2 to 3 years and then annually.
• Targeted adjuvant therapy substantially improved outcomes for resected EGFR–mutant or ALK–rearranged non-small cell lung cancer (NSCLC). Four-year DFS was increased to 70% with osimertinib compared with 29% with placebo (hazard ratio [HR], 0.23) and 5–year overall survival was increased to 85% compared with 73% (HR, 0.49). Two–year DFS was 93.8% with alectinib compared with 63% with placebo (HR, 0.24). In unresectable EGFR-mutated stage III NSCLC, median progression-free survival was 39.1 months with adjuvant osimertinib compared with 5.6 months with placebo. For resected ALKpositive disease, 2–year DFS was 93.8% with adjuvant alectinib compared with 63.0% with chemotherapy (HR, 0.24).
• However, singleagent single agent programmed cell death protein 1 inhibitors or programmed death-ligand 1 inhibitors demonstrated limited efficacy in EGFR or ALK–driven tumors, and benefit was attenuated in never smokers. Regarding screening and early detection, the US Preventive Services Task Force did not recommend lowdose CT screening for nonsmokers, whereas Taiwan implemented a biennial screening program for selected nonsmoking high–risk groups.

IN PRACTICE:

“Among patients with lung cancer, nonsmoking individuals are more likely to have genomic alterations, such as EGFR mutations or ALK gene rearrangements, and these patients have improved survival when treated with TKIs compared with chemotherapy,” the authors of the study wrote.

SOURCE:

The study, led by Cian Murphy, PhD, Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute, London, England, was published online in JAMA.

LIMITATIONS:

Becausesmoking history was often not included in many databases, cancer registries, and trials, the incidence and prevalence of lung cancer in nonsmokers could not be accurately determined. Additionally, accurate quantification of environmental exposures, such as air pollution, presented significant challenges. The quality of the evidence was not formally evaluated, and some relevant articles may have been missed in the literature review.

DISCLOSURES:

The study received support from multiple organizations, including the Rosetrees Trust, Ruth Strauss Foundation, Cancer Research UK, and the National Health and Medical Research Council. Several authors reported receiving grants or personal fees from and having other ties with various sources. Full disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

TOPLINE:

A comprehensive review of 92 studies found that 15% to 20% of lung cancers occurred among nonsmokers and were associated with environmental and germline risk factors. These cancers frequently harbored actionable genomic drivers, and targeted EGFR and ALK therapies produced significant diseasefree survival (DFS) and overall survival benefits.

METHODOLOGY:

• Lung cancer continues to be the leading cause of cancer death worldwide, causing about 1.8 million deaths in 2022, with smoking remaining the predominant risk factor. However, the incidence of lung cancer among nonsmokers (those who have smoked less than 100 cigarettes in their lifetime) is rising, varies by sex and geography, and is linked to environmental exposures and family history. The misperception that lung cancer is almost invariably caused by smoking may delay assessment and diagnosis.
• Researchers conducted a review of 92 studies on lung cancer in nonsmokers: 6 meta-analyses or systematic reviews, 16 randomized clinical trials, eight prospective cohort studies, seven retrospective cohort studies, three cross-sectional studies, four observational or case-control studies, 13 genomic studies, and 35 other studies.
• Overall, lung cancer among nonsmokers accounted for 15% to 20% of all lung cancer cases. Most lung cancers in nonsmokers were adenocarcinomas (60% to 80%), with a median age at diagnosis of 67 years in this group compared with 70 years in people with a history of smoking.
• Data analysis from three US hospital networks showed that the proportion of lung cancer among nonsmokers increased from 8.0% to 14.9% between 1990 and 2013. A pooled analysis of seven Finnish cohorts reported an absolute increase in lung cancer among nonsmokers from 6.9 per 100,000 person-years in 1972 to 12.9 per 100,000 person-years in 2015.
• The age-adjusted incidence rate of lung cancer in the US between 2000 and 2013 was 17.5 per 100,000 individuals among Asian female nonsmokers compared with 10.1 per 100,000 among non-Hispanic White female nonsmokers.

TAKEAWAY:

• Environmental and occupational risk factors were secondhand smoke, residential radon, outdoor and household air pollution (PM_2.5), asbestos and silica exposure, and prior thoracic radiotherapy. Having a first-degree relative with lung cancer increased the risk of developing lung cancer, and genome-wide association studies identified susceptibility loci associated with lung cancer risk in nonsmokers.
• Family history and inherited susceptibility increased lung cancer risk in never smokers (odds ratio [OR] for lung cancer in those with a first–degree relative, 1.51), and clonal hematopoiesis was also associated with higher risk (OR, 1.43). Importantly, tumors in nonsmokers were frequently driven by actionable somatic alterations (EGFR mutations, 40% to 60% in nonsmokers compared with 10% in smokers) and enrichment of ALK/ROS1/RET/ERBB2/NTRK/NRG1 fusions; 78% to 92% of adenocarcinomas in nonsmokers harbored actionable drivers (compared with 49.5% in ever smokers), and nonsmokers had a substantially lower tumor mutational burden (10–fold lower).
• Similar to individuals with a history of smoking, nonsmokers with lung cancer presented with cough, pain, dyspnea, or weight loss or had disease detected incidentally. Surgical resection remained the preferred treatment for anatomically resectable lung cancer (stages I-III) in medically eligible patients, with follow-up CT screening recommended every 6 months for 2 to 3 years and then annually.
• Targeted adjuvant therapy substantially improved outcomes for resected EGFR–mutant or ALK–rearranged non-small cell lung cancer (NSCLC). Four-year DFS was increased to 70% with osimertinib compared with 29% with placebo (hazard ratio [HR], 0.23) and 5–year overall survival was increased to 85% compared with 73% (HR, 0.49). Two–year DFS was 93.8% with alectinib compared with 63% with placebo (HR, 0.24). In unresectable EGFR-mutated stage III NSCLC, median progression-free survival was 39.1 months with adjuvant osimertinib compared with 5.6 months with placebo. For resected ALKpositive disease, 2–year DFS was 93.8% with adjuvant alectinib compared with 63.0% with chemotherapy (HR, 0.24).
• However, singleagent single agent programmed cell death protein 1 inhibitors or programmed death-ligand 1 inhibitors demonstrated limited efficacy in EGFR or ALK–driven tumors, and benefit was attenuated in never smokers. Regarding screening and early detection, the US Preventive Services Task Force did not recommend lowdose CT screening for nonsmokers, whereas Taiwan implemented a biennial screening program for selected nonsmoking high–risk groups.

IN PRACTICE:

“Among patients with lung cancer, nonsmoking individuals are more likely to have genomic alterations, such as EGFR mutations or ALK gene rearrangements, and these patients have improved survival when treated with TKIs compared with chemotherapy,” the authors of the study wrote.

SOURCE:

The study, led by Cian Murphy, PhD, Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute, London, England, was published online in JAMA.

LIMITATIONS:

Becausesmoking history was often not included in many databases, cancer registries, and trials, the incidence and prevalence of lung cancer in nonsmokers could not be accurately determined. Additionally, accurate quantification of environmental exposures, such as air pollution, presented significant challenges. The quality of the evidence was not formally evaluated, and some relevant articles may have been missed in the literature review.

DISCLOSURES:

The study received support from multiple organizations, including the Rosetrees Trust, Ruth Strauss Foundation, Cancer Research UK, and the National Health and Medical Research Council. Several authors reported receiving grants or personal fees from and having other ties with various sources. Full disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

TOPLINE:

A comprehensive review of 92 studies found that 15% to 20% of lung cancers occurred among nonsmokers and were associated with environmental and germline risk factors. These cancers frequently harbored actionable genomic drivers, and targeted EGFR and ALK therapies produced significant diseasefree survival (DFS) and overall survival benefits.

METHODOLOGY:

• Lung cancer continues to be the leading cause of cancer death worldwide, causing about 1.8 million deaths in 2022, with smoking remaining the predominant risk factor. However, the incidence of lung cancer among nonsmokers (those who have smoked less than 100 cigarettes in their lifetime) is rising, varies by sex and geography, and is linked to environmental exposures and family history. The misperception that lung cancer is almost invariably caused by smoking may delay assessment and diagnosis.
• Researchers conducted a review of 92 studies on lung cancer in nonsmokers: 6 meta-analyses or systematic reviews, 16 randomized clinical trials, eight prospective cohort studies, seven retrospective cohort studies, three cross-sectional studies, four observational or case-control studies, 13 genomic studies, and 35 other studies.
• Overall, lung cancer among nonsmokers accounted for 15% to 20% of all lung cancer cases. Most lung cancers in nonsmokers were adenocarcinomas (60% to 80%), with a median age at diagnosis of 67 years in this group compared with 70 years in people with a history of smoking.
• Data analysis from three US hospital networks showed that the proportion of lung cancer among nonsmokers increased from 8.0% to 14.9% between 1990 and 2013. A pooled analysis of seven Finnish cohorts reported an absolute increase in lung cancer among nonsmokers from 6.9 per 100,000 person-years in 1972 to 12.9 per 100,000 person-years in 2015.
• The age-adjusted incidence rate of lung cancer in the US between 2000 and 2013 was 17.5 per 100,000 individuals among Asian female nonsmokers compared with 10.1 per 100,000 among non-Hispanic White female nonsmokers.

TAKEAWAY:

• Environmental and occupational risk factors were secondhand smoke, residential radon, outdoor and household air pollution (PM_2.5), asbestos and silica exposure, and prior thoracic radiotherapy. Having a first-degree relative with lung cancer increased the risk of developing lung cancer, and genome-wide association studies identified susceptibility loci associated with lung cancer risk in nonsmokers.
• Family history and inherited susceptibility increased lung cancer risk in never smokers (odds ratio [OR] for lung cancer in those with a first–degree relative, 1.51), and clonal hematopoiesis was also associated with higher risk (OR, 1.43). Importantly, tumors in nonsmokers were frequently driven by actionable somatic alterations (EGFR mutations, 40% to 60% in nonsmokers compared with 10% in smokers) and enrichment of ALK/ROS1/RET/ERBB2/NTRK/NRG1 fusions; 78% to 92% of adenocarcinomas in nonsmokers harbored actionable drivers (compared with 49.5% in ever smokers), and nonsmokers had a substantially lower tumor mutational burden (10–fold lower).
• Similar to individuals with a history of smoking, nonsmokers with lung cancer presented with cough, pain, dyspnea, or weight loss or had disease detected incidentally. Surgical resection remained the preferred treatment for anatomically resectable lung cancer (stages I-III) in medically eligible patients, with follow-up CT screening recommended every 6 months for 2 to 3 years and then annually.
• Targeted adjuvant therapy substantially improved outcomes for resected EGFR–mutant or ALK–rearranged non-small cell lung cancer (NSCLC). Four-year DFS was increased to 70% with osimertinib compared with 29% with placebo (hazard ratio [HR], 0.23) and 5–year overall survival was increased to 85% compared with 73% (HR, 0.49). Two–year DFS was 93.8% with alectinib compared with 63% with placebo (HR, 0.24). In unresectable EGFR-mutated stage III NSCLC, median progression-free survival was 39.1 months with adjuvant osimertinib compared with 5.6 months with placebo. For resected ALKpositive disease, 2–year DFS was 93.8% with adjuvant alectinib compared with 63.0% with chemotherapy (HR, 0.24).
• However, singleagent single agent programmed cell death protein 1 inhibitors or programmed death-ligand 1 inhibitors demonstrated limited efficacy in EGFR or ALK–driven tumors, and benefit was attenuated in never smokers. Regarding screening and early detection, the US Preventive Services Task Force did not recommend lowdose CT screening for nonsmokers, whereas Taiwan implemented a biennial screening program for selected nonsmoking high–risk groups.

IN PRACTICE:

“Among patients with lung cancer, nonsmoking individuals are more likely to have genomic alterations, such as EGFR mutations or ALK gene rearrangements, and these patients have improved survival when treated with TKIs compared with chemotherapy,” the authors of the study wrote.

SOURCE:

The study, led by Cian Murphy, PhD, Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute, London, England, was published online in JAMA.

LIMITATIONS:

Becausesmoking history was often not included in many databases, cancer registries, and trials, the incidence and prevalence of lung cancer in nonsmokers could not be accurately determined. Additionally, accurate quantification of environmental exposures, such as air pollution, presented significant challenges. The quality of the evidence was not formally evaluated, and some relevant articles may have been missed in the literature review.

DISCLOSURES:

The study received support from multiple organizations, including the Rosetrees Trust, Ruth Strauss Foundation, Cancer Research UK, and the National Health and Medical Research Council. Several authors reported receiving grants or personal fees from and having other ties with various sources. Full disclosures are noted in the original article.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

A version of this article first appeared on Medscape.com.

A faster, oral alternative to docetaxel is set to reach NHS clinics after the National Institute for Health and Care Excellence (NICE) recommended darolutamide (Nubeqa, Bayer) in combination with androgen deprivation therapy (ADT) for men with metastatic hormone-sensitive prostate cancer who are unable to receive or tolerate chemotherapy.

Detailed in NICE’s final draft guidance, the decision will make darolutamide available through the NHS in England and Wales to approximately 6000 patients, offering a new oral therapy for those who with limited alternatives to docetaxel or other androgen-receptor inhibitors.

 

New Option for Chemo-Ineligible Patients

Darolutamide functions by blocking hormones that fuel cancer growth, specifically depriving prostate cancer cells of testosterone required for multiplication and spread. Patients take two tablets twice daily alongside standard ADT. 

Peter Johnson, national clinical director for cancer at NHS England, welcomed the decision and expects this approval to give clinicians and their patients “more flexibility to choose the approach best suited to individual circumstances and clinical needs.”

The guidance was finalised 5 weeks ahead of the standard review timeline, underscoring NICE’s commitment to accelerating access to effective prostate cancer treatments.

 

Clinical Trial Evidence

The NICE’s decision was supported by evidence from the phase 3 ARASENS trial (N = 1306). 

The results showed that adding darolutamide to ADT and docetaxel significantly improved overall survival in metastatic hormone-sensitive prostate cancer, reducing the risk for death by 32% compared with ADT and docetaxel alone. Progression-free outcomes, measured by time to castration-resistant disease or death, also favoured darolutamide. 

A NICE network meta-analysis of the TITAN, ARCHES, LATITUDE, and STAMPEDE trials suggested that combining ADT with androgen-receptor pathway inhibitors such as apalutamide, enzalutamide, and abiraterone provides comparable survival benefits in this disease setting.

 

Cost and Implementation

NICE determined that darolutamide plus ADT delivers similar or lower overall costs to the NHS compared with apalutamide plus ADT. The list price is £4040.00 for a 28-day supply (112 × 300-mg tablets), though Bayer has agreed to a confidential commercial discount.

The guidance requires healthcare providers to use the least expensive suitable treatment option, considering administration costs, dosages, price per dose, and commercial arrangements when choosing between darolutamide plus ADT and apalutamide plus ADT. 

NHS England and integrated care boards must provide funding within 30 days of final publication, with routine commissioning beginning after this interim period.

A version of this article first appeared on Medscape.com.

A faster, oral alternative to docetaxel is set to reach NHS clinics after the National Institute for Health and Care Excellence (NICE) recommended darolutamide (Nubeqa, Bayer) in combination with androgen deprivation therapy (ADT) for men with metastatic hormone-sensitive prostate cancer who are unable to receive or tolerate chemotherapy.

Detailed in NICE’s final draft guidance, the decision will make darolutamide available through the NHS in England and Wales to approximately 6000 patients, offering a new oral therapy for those who with limited alternatives to docetaxel or other androgen-receptor inhibitors.

 

New Option for Chemo-Ineligible Patients

Darolutamide functions by blocking hormones that fuel cancer growth, specifically depriving prostate cancer cells of testosterone required for multiplication and spread. Patients take two tablets twice daily alongside standard ADT. 

Peter Johnson, national clinical director for cancer at NHS England, welcomed the decision and expects this approval to give clinicians and their patients “more flexibility to choose the approach best suited to individual circumstances and clinical needs.”

The guidance was finalised 5 weeks ahead of the standard review timeline, underscoring NICE’s commitment to accelerating access to effective prostate cancer treatments.

 

Clinical Trial Evidence

The NICE’s decision was supported by evidence from the phase 3 ARASENS trial (N = 1306). 

The results showed that adding darolutamide to ADT and docetaxel significantly improved overall survival in metastatic hormone-sensitive prostate cancer, reducing the risk for death by 32% compared with ADT and docetaxel alone. Progression-free outcomes, measured by time to castration-resistant disease or death, also favoured darolutamide. 

A NICE network meta-analysis of the TITAN, ARCHES, LATITUDE, and STAMPEDE trials suggested that combining ADT with androgen-receptor pathway inhibitors such as apalutamide, enzalutamide, and abiraterone provides comparable survival benefits in this disease setting.

 

Cost and Implementation

NICE determined that darolutamide plus ADT delivers similar or lower overall costs to the NHS compared with apalutamide plus ADT. The list price is £4040.00 for a 28-day supply (112 × 300-mg tablets), though Bayer has agreed to a confidential commercial discount.

The guidance requires healthcare providers to use the least expensive suitable treatment option, considering administration costs, dosages, price per dose, and commercial arrangements when choosing between darolutamide plus ADT and apalutamide plus ADT. 

NHS England and integrated care boards must provide funding within 30 days of final publication, with routine commissioning beginning after this interim period.

A version of this article first appeared on Medscape.com.

A faster, oral alternative to docetaxel is set to reach NHS clinics after the National Institute for Health and Care Excellence (NICE) recommended darolutamide (Nubeqa, Bayer) in combination with androgen deprivation therapy (ADT) for men with metastatic hormone-sensitive prostate cancer who are unable to receive or tolerate chemotherapy.

Detailed in NICE’s final draft guidance, the decision will make darolutamide available through the NHS in England and Wales to approximately 6000 patients, offering a new oral therapy for those who with limited alternatives to docetaxel or other androgen-receptor inhibitors.

 

New Option for Chemo-Ineligible Patients

Darolutamide functions by blocking hormones that fuel cancer growth, specifically depriving prostate cancer cells of testosterone required for multiplication and spread. Patients take two tablets twice daily alongside standard ADT. 

Peter Johnson, national clinical director for cancer at NHS England, welcomed the decision and expects this approval to give clinicians and their patients “more flexibility to choose the approach best suited to individual circumstances and clinical needs.”

The guidance was finalised 5 weeks ahead of the standard review timeline, underscoring NICE’s commitment to accelerating access to effective prostate cancer treatments.

 

Clinical Trial Evidence

The NICE’s decision was supported by evidence from the phase 3 ARASENS trial (N = 1306). 

The results showed that adding darolutamide to ADT and docetaxel significantly improved overall survival in metastatic hormone-sensitive prostate cancer, reducing the risk for death by 32% compared with ADT and docetaxel alone. Progression-free outcomes, measured by time to castration-resistant disease or death, also favoured darolutamide. 

A NICE network meta-analysis of the TITAN, ARCHES, LATITUDE, and STAMPEDE trials suggested that combining ADT with androgen-receptor pathway inhibitors such as apalutamide, enzalutamide, and abiraterone provides comparable survival benefits in this disease setting.

 

Cost and Implementation

NICE determined that darolutamide plus ADT delivers similar or lower overall costs to the NHS compared with apalutamide plus ADT. The list price is £4040.00 for a 28-day supply (112 × 300-mg tablets), though Bayer has agreed to a confidential commercial discount.

The guidance requires healthcare providers to use the least expensive suitable treatment option, considering administration costs, dosages, price per dose, and commercial arrangements when choosing between darolutamide plus ADT and apalutamide plus ADT. 

NHS England and integrated care boards must provide funding within 30 days of final publication, with routine commissioning beginning after this interim period.

A version of this article first appeared on Medscape.com.

Repeated and frequent hospitalizations—sometimes referred to as the revolving door phenomenon— are a particular risk for patients during the first month after discharge. Early psychiatric readmission is a standard indicator of adverse outcomes. However, the results 

The quality of previous care has long been thought to be a driver of readmission. If that’s the case, a 2025 study suggests that on average veterans received high-quality inpatient psychiatric services at Veterans Health Administration (VHA) facilities across the nation and that may have been key to keeping readmissions down. Analyzing data from 88,954 veterans who received care at VHA Inpatient Mental Health (IMH) services, the researchers found a “relatively low” rate of readmission within 30 days: 7.1% compared with 8% to 31% of other psychiatric patients in the US. With 40,220 unique patients receiving IMH care per year on average between October 2019 and September 2022, a 7.1% readmission rate means > 2800 30-day readmissions annually.

Research has found that veterans who receive care at the VA have better outcomes than those treated in the private sector. Part of that has to do with practitioners who understand the unique needs of their patients. Veterans may have posttraumatic stress disorder or multiple diagnoses, such as depression, panic disorder, and a substance use disorder. Their mental health issues may also coexist with physical health problems, such as traumatic brain injuries due to explosions.

“If you’re trained at the VA, you learn something important about veteran mental health care that you’ll never get if you’re trained someplace else,” Rodney R. Baker, PhD, retired mental health director and chief of psychology for the South Texas VA Health Care System, said recently. Community clinicians may not know how to collect and incorporate information about a patient’s military history, including details about deployments, combat exposure, injuries, military sexual trauma, and unit culture. They may also lack expertise in navigating the transition between military and veteran life, now considered a critical adjustment period.

“This is a unique population,” said Conwell Smith, the American Psychological Association’s deputy chief of military and veteran policy. “Sending veterans out to the community without requiring that mental health care providers understand them is concerning.”

IMH services aim to stabilize mental health crises and improve veterans’ functioning through patient-centered, evidence-based, and recovery-oriented approaches shown to reduce readmission rates. Treatment generally involves a minimum of 4 hours of interdisciplinary, therapeutic programming each day. And upon discharge, the inpatient care team facilitates the patient’s transition to appropriate outpatient services.

Follow-up care, particularly during the first 30 days, has proved critical in reducing readmissions. In studies that have analyzed postdischarge interventions (psychoeducation, mentoring, community-based hospital treatment, use of continuous follow-up and compulsory community treatment), all found fewer hospitalizations when compared to a control group, or a smaller number of admissions after the intervention. 

Mental health care for veterans should be provided by experienced practitioners—but those practitioners are leaving VA. According to the VA Office of Inspector General, 57% of medical centers report a shortage of psychologists. And according to the VA’s monthly Workforce Dashboard, the VHA lost 234 psychologists in the first 9 months of 2025. The VA has also announced plans to cut 30,000 jobs by the end of the year and impose caps on staff at every medical center.

“This approach locks in permanent VA understaffing just as demand for mental health services is projected to continue growing through 2030,” said Russell Lemle, PhD, a clinical psychologist and senior policy analyst for the Veterans Healthcare Policy Institute. “The private sector can’t fill this gap either—over a third of Americans live in areas already facing mental health professional shortages. That’s not taking care of our veterans.

“Unless actions are taken quickly to reverse the trend, its mental health services could easily diminish substantially within 10 to 20 years.”

Repeated and frequent hospitalizations—sometimes referred to as the revolving door phenomenon— are a particular risk for patients during the first month after discharge. Early psychiatric readmission is a standard indicator of adverse outcomes. However, the results 

The quality of previous care has long been thought to be a driver of readmission. If that’s the case, a 2025 study suggests that on average veterans received high-quality inpatient psychiatric services at Veterans Health Administration (VHA) facilities across the nation and that may have been key to keeping readmissions down. Analyzing data from 88,954 veterans who received care at VHA Inpatient Mental Health (IMH) services, the researchers found a “relatively low” rate of readmission within 30 days: 7.1% compared with 8% to 31% of other psychiatric patients in the US. With 40,220 unique patients receiving IMH care per year on average between October 2019 and September 2022, a 7.1% readmission rate means > 2800 30-day readmissions annually.

Research has found that veterans who receive care at the VA have better outcomes than those treated in the private sector. Part of that has to do with practitioners who understand the unique needs of their patients. Veterans may have posttraumatic stress disorder or multiple diagnoses, such as depression, panic disorder, and a substance use disorder. Their mental health issues may also coexist with physical health problems, such as traumatic brain injuries due to explosions.

“If you’re trained at the VA, you learn something important about veteran mental health care that you’ll never get if you’re trained someplace else,” Rodney R. Baker, PhD, retired mental health director and chief of psychology for the South Texas VA Health Care System, said recently. Community clinicians may not know how to collect and incorporate information about a patient’s military history, including details about deployments, combat exposure, injuries, military sexual trauma, and unit culture. They may also lack expertise in navigating the transition between military and veteran life, now considered a critical adjustment period.

“This is a unique population,” said Conwell Smith, the American Psychological Association’s deputy chief of military and veteran policy. “Sending veterans out to the community without requiring that mental health care providers understand them is concerning.”

IMH services aim to stabilize mental health crises and improve veterans’ functioning through patient-centered, evidence-based, and recovery-oriented approaches shown to reduce readmission rates. Treatment generally involves a minimum of 4 hours of interdisciplinary, therapeutic programming each day. And upon discharge, the inpatient care team facilitates the patient’s transition to appropriate outpatient services.

Follow-up care, particularly during the first 30 days, has proved critical in reducing readmissions. In studies that have analyzed postdischarge interventions (psychoeducation, mentoring, community-based hospital treatment, use of continuous follow-up and compulsory community treatment), all found fewer hospitalizations when compared to a control group, or a smaller number of admissions after the intervention. 

Mental health care for veterans should be provided by experienced practitioners—but those practitioners are leaving VA. According to the VA Office of Inspector General, 57% of medical centers report a shortage of psychologists. And according to the VA’s monthly Workforce Dashboard, the VHA lost 234 psychologists in the first 9 months of 2025. The VA has also announced plans to cut 30,000 jobs by the end of the year and impose caps on staff at every medical center.

“This approach locks in permanent VA understaffing just as demand for mental health services is projected to continue growing through 2030,” said Russell Lemle, PhD, a clinical psychologist and senior policy analyst for the Veterans Healthcare Policy Institute. “The private sector can’t fill this gap either—over a third of Americans live in areas already facing mental health professional shortages. That’s not taking care of our veterans.

“Unless actions are taken quickly to reverse the trend, its mental health services could easily diminish substantially within 10 to 20 years.”

Repeated and frequent hospitalizations—sometimes referred to as the revolving door phenomenon— are a particular risk for patients during the first month after discharge. Early psychiatric readmission is a standard indicator of adverse outcomes. However, the results 

The quality of previous care has long been thought to be a driver of readmission. If that’s the case, a 2025 study suggests that on average veterans received high-quality inpatient psychiatric services at Veterans Health Administration (VHA) facilities across the nation and that may have been key to keeping readmissions down. Analyzing data from 88,954 veterans who received care at VHA Inpatient Mental Health (IMH) services, the researchers found a “relatively low” rate of readmission within 30 days: 7.1% compared with 8% to 31% of other psychiatric patients in the US. With 40,220 unique patients receiving IMH care per year on average between October 2019 and September 2022, a 7.1% readmission rate means > 2800 30-day readmissions annually.

Research has found that veterans who receive care at the VA have better outcomes than those treated in the private sector. Part of that has to do with practitioners who understand the unique needs of their patients. Veterans may have posttraumatic stress disorder or multiple diagnoses, such as depression, panic disorder, and a substance use disorder. Their mental health issues may also coexist with physical health problems, such as traumatic brain injuries due to explosions.

“If you’re trained at the VA, you learn something important about veteran mental health care that you’ll never get if you’re trained someplace else,” Rodney R. Baker, PhD, retired mental health director and chief of psychology for the South Texas VA Health Care System, said recently. Community clinicians may not know how to collect and incorporate information about a patient’s military history, including details about deployments, combat exposure, injuries, military sexual trauma, and unit culture. They may also lack expertise in navigating the transition between military and veteran life, now considered a critical adjustment period.

“This is a unique population,” said Conwell Smith, the American Psychological Association’s deputy chief of military and veteran policy. “Sending veterans out to the community without requiring that mental health care providers understand them is concerning.”

IMH services aim to stabilize mental health crises and improve veterans’ functioning through patient-centered, evidence-based, and recovery-oriented approaches shown to reduce readmission rates. Treatment generally involves a minimum of 4 hours of interdisciplinary, therapeutic programming each day. And upon discharge, the inpatient care team facilitates the patient’s transition to appropriate outpatient services.

Follow-up care, particularly during the first 30 days, has proved critical in reducing readmissions. In studies that have analyzed postdischarge interventions (psychoeducation, mentoring, community-based hospital treatment, use of continuous follow-up and compulsory community treatment), all found fewer hospitalizations when compared to a control group, or a smaller number of admissions after the intervention. 

Mental health care for veterans should be provided by experienced practitioners—but those practitioners are leaving VA. According to the VA Office of Inspector General, 57% of medical centers report a shortage of psychologists. And according to the VA’s monthly Workforce Dashboard, the VHA lost 234 psychologists in the first 9 months of 2025. The VA has also announced plans to cut 30,000 jobs by the end of the year and impose caps on staff at every medical center.

“This approach locks in permanent VA understaffing just as demand for mental health services is projected to continue growing through 2030,” said Russell Lemle, PhD, a clinical psychologist and senior policy analyst for the Veterans Healthcare Policy Institute. “The private sector can’t fill this gap either—over a third of Americans live in areas already facing mental health professional shortages. That’s not taking care of our veterans.

“Unless actions are taken quickly to reverse the trend, its mental health services could easily diminish substantially within 10 to 20 years.”

BERLIN — Adding durvalumab (Imfinzi) to the standard perioperative regimen for patients with early adenocarcinoma of the upper gastrointestinal tract improves overall survival, according to findings presented at the 2025 annual meeting of the European Society for Medical Oncology (ESMO).

Experts said the survival benefit further supports perioperative durvalumab plus FLOT (fluorouracil, leucovorin, oxaliplatin, and docetaxel) as the new standard of care for patients with localized gastric or gastroesophageal adenocarcinoma. Earlier results from the phase 3 MATTERHORN trial, reported at the American Society of Clinical Oncology meeting (ASCO) in June, showed that the addition of durvalumab improved event-free survival compared with FLOT alone. 

The findings presented at ESMO show that at 36 months, overall survival was 68.6% among patients who received durvalumab + FLOT vs 61.9% among those given FLOT plus a placebo. After a median of 43 months, the survival advantage in the durvalumab group was statistically significant (hazard ratio [HR], 0.78; 95% CI, 0.63-0.96; P = .021) and “more importantly, clinically meaningful,” said lead investigator Josep Tabernero, MD, PhD, of Vall d’Hebron University Hospital in Barcelona.

The results “strongly support the use of perioperative durvalumab plus chemotherapy with FLOT as a new global standard of care for patients with localized gastric and gastroesophageal adenocarcinoma,” Tabernero said. 

Speaking as discussant for the session, Sylvie Lorenzen, MD, PhD, Technische Universität München in Munich, Germany, was enthusiastic that the previously reported trends in MATTERHORN held strong.

“The shape of the curves presented at ASCO was already very positive,” she said. “And now, with a longer follow-up, more events, and a higher overall survival maturity, they reach statistical significance. It looks like the magnitude of the effect increases with longer follow-up, and this is important for our patients.” 

The trial randomly assigned 948 patients with resectable gastric or gastroesophageal adenocarcinoma to receive either durvalumab (1500 mg) or placebo every 4 weeks, plus FLOT for 2 cycles before surgery and then again after, followed by durvalumab or placebo every 4 weeks for 10 cycles. 

Patients were stratified according to lymph node status, as well as PD-L1 expression (≥ 1% or < 1%, according to the Tumor Area Positivity score.)

The improvement in overall survival with durvalumab was seen regardless of PD-L1 expression, Tabernero said, with the same hazard ratios (0.79) in both the positive and negative subgroups.

However, there was no clear overall survival benefit in certain other subgroups, including women (n = 266; HR, 0.91), those with node-negative disease (n = 277; HR, 1.01), and those with diffuse histology (n = 249; HR, 0.98).

Lorenzen said that clinicians should “pay attention” to those patient subgroups, as they seem to benefit less from the addition of durvalumab. However, she cautioned that the findings were based on small patient numbers and the confidence intervals were wide.

Tabernero also reported additional data on event-free survival (EFS). Overall, the durvalumab/FLOT combination improved EFS among patients with any degree of pathological response and irrespective of lymph node status at surgery.

Regarding nodal staging, which was done in 800 patients, the percentage who achieved negative nodal status was higher in the durvalumab group (58.2%) vs the placebo group (44.8%). However, the improvement in EFS with durvalumab was comparable for node-negative (HR, 0.74) and node-positive (HR, 0.77) patients.

Lorenzen said that overall, the results provide a solid answer to the question, “Is it time to change practice?”

“I think MATTERHORN gives us the largest dataset and answers this question satisfactorily,” she said. Given that overall survival improved regardless of PD-L1 expression, she added, the combination of durvalumab and FLOT should be offered to “all our patient subgroups.”

The study was funded by AstraZeneca. Tabernero made numerous disclosures, including relationships with AstraZeneca, Boehringer Ingelheim, Chugai, and Daichii Sankyo. Lorenzen disclosed financial interests in or serving as an invited speaker for Servier, Lilly, MSD, and BMS.

A version of this article appeared on Medscape.com . 

BERLIN — Adding durvalumab (Imfinzi) to the standard perioperative regimen for patients with early adenocarcinoma of the upper gastrointestinal tract improves overall survival, according to findings presented at the 2025 annual meeting of the European Society for Medical Oncology (ESMO).

Experts said the survival benefit further supports perioperative durvalumab plus FLOT (fluorouracil, leucovorin, oxaliplatin, and docetaxel) as the new standard of care for patients with localized gastric or gastroesophageal adenocarcinoma. Earlier results from the phase 3 MATTERHORN trial, reported at the American Society of Clinical Oncology meeting (ASCO) in June, showed that the addition of durvalumab improved event-free survival compared with FLOT alone. 

The findings presented at ESMO show that at 36 months, overall survival was 68.6% among patients who received durvalumab + FLOT vs 61.9% among those given FLOT plus a placebo. After a median of 43 months, the survival advantage in the durvalumab group was statistically significant (hazard ratio [HR], 0.78; 95% CI, 0.63-0.96; P = .021) and “more importantly, clinically meaningful,” said lead investigator Josep Tabernero, MD, PhD, of Vall d’Hebron University Hospital in Barcelona.

The results “strongly support the use of perioperative durvalumab plus chemotherapy with FLOT as a new global standard of care for patients with localized gastric and gastroesophageal adenocarcinoma,” Tabernero said. 

Speaking as discussant for the session, Sylvie Lorenzen, MD, PhD, Technische Universität München in Munich, Germany, was enthusiastic that the previously reported trends in MATTERHORN held strong.

“The shape of the curves presented at ASCO was already very positive,” she said. “And now, with a longer follow-up, more events, and a higher overall survival maturity, they reach statistical significance. It looks like the magnitude of the effect increases with longer follow-up, and this is important for our patients.” 

The trial randomly assigned 948 patients with resectable gastric or gastroesophageal adenocarcinoma to receive either durvalumab (1500 mg) or placebo every 4 weeks, plus FLOT for 2 cycles before surgery and then again after, followed by durvalumab or placebo every 4 weeks for 10 cycles. 

Patients were stratified according to lymph node status, as well as PD-L1 expression (≥ 1% or < 1%, according to the Tumor Area Positivity score.)

The improvement in overall survival with durvalumab was seen regardless of PD-L1 expression, Tabernero said, with the same hazard ratios (0.79) in both the positive and negative subgroups.

However, there was no clear overall survival benefit in certain other subgroups, including women (n = 266; HR, 0.91), those with node-negative disease (n = 277; HR, 1.01), and those with diffuse histology (n = 249; HR, 0.98).

Lorenzen said that clinicians should “pay attention” to those patient subgroups, as they seem to benefit less from the addition of durvalumab. However, she cautioned that the findings were based on small patient numbers and the confidence intervals were wide.

Tabernero also reported additional data on event-free survival (EFS). Overall, the durvalumab/FLOT combination improved EFS among patients with any degree of pathological response and irrespective of lymph node status at surgery.

Regarding nodal staging, which was done in 800 patients, the percentage who achieved negative nodal status was higher in the durvalumab group (58.2%) vs the placebo group (44.8%). However, the improvement in EFS with durvalumab was comparable for node-negative (HR, 0.74) and node-positive (HR, 0.77) patients.

Lorenzen said that overall, the results provide a solid answer to the question, “Is it time to change practice?”

“I think MATTERHORN gives us the largest dataset and answers this question satisfactorily,” she said. Given that overall survival improved regardless of PD-L1 expression, she added, the combination of durvalumab and FLOT should be offered to “all our patient subgroups.”

The study was funded by AstraZeneca. Tabernero made numerous disclosures, including relationships with AstraZeneca, Boehringer Ingelheim, Chugai, and Daichii Sankyo. Lorenzen disclosed financial interests in or serving as an invited speaker for Servier, Lilly, MSD, and BMS.

A version of this article appeared on Medscape.com . 

BERLIN — Adding durvalumab (Imfinzi) to the standard perioperative regimen for patients with early adenocarcinoma of the upper gastrointestinal tract improves overall survival, according to findings presented at the 2025 annual meeting of the European Society for Medical Oncology (ESMO).

Experts said the survival benefit further supports perioperative durvalumab plus FLOT (fluorouracil, leucovorin, oxaliplatin, and docetaxel) as the new standard of care for patients with localized gastric or gastroesophageal adenocarcinoma. Earlier results from the phase 3 MATTERHORN trial, reported at the American Society of Clinical Oncology meeting (ASCO) in June, showed that the addition of durvalumab improved event-free survival compared with FLOT alone. 

The findings presented at ESMO show that at 36 months, overall survival was 68.6% among patients who received durvalumab + FLOT vs 61.9% among those given FLOT plus a placebo. After a median of 43 months, the survival advantage in the durvalumab group was statistically significant (hazard ratio [HR], 0.78; 95% CI, 0.63-0.96; P = .021) and “more importantly, clinically meaningful,” said lead investigator Josep Tabernero, MD, PhD, of Vall d’Hebron University Hospital in Barcelona.

The results “strongly support the use of perioperative durvalumab plus chemotherapy with FLOT as a new global standard of care for patients with localized gastric and gastroesophageal adenocarcinoma,” Tabernero said. 

Speaking as discussant for the session, Sylvie Lorenzen, MD, PhD, Technische Universität München in Munich, Germany, was enthusiastic that the previously reported trends in MATTERHORN held strong.

“The shape of the curves presented at ASCO was already very positive,” she said. “And now, with a longer follow-up, more events, and a higher overall survival maturity, they reach statistical significance. It looks like the magnitude of the effect increases with longer follow-up, and this is important for our patients.” 

The trial randomly assigned 948 patients with resectable gastric or gastroesophageal adenocarcinoma to receive either durvalumab (1500 mg) or placebo every 4 weeks, plus FLOT for 2 cycles before surgery and then again after, followed by durvalumab or placebo every 4 weeks for 10 cycles. 

Patients were stratified according to lymph node status, as well as PD-L1 expression (≥ 1% or < 1%, according to the Tumor Area Positivity score.)

The improvement in overall survival with durvalumab was seen regardless of PD-L1 expression, Tabernero said, with the same hazard ratios (0.79) in both the positive and negative subgroups.

However, there was no clear overall survival benefit in certain other subgroups, including women (n = 266; HR, 0.91), those with node-negative disease (n = 277; HR, 1.01), and those with diffuse histology (n = 249; HR, 0.98).

Lorenzen said that clinicians should “pay attention” to those patient subgroups, as they seem to benefit less from the addition of durvalumab. However, she cautioned that the findings were based on small patient numbers and the confidence intervals were wide.

Tabernero also reported additional data on event-free survival (EFS). Overall, the durvalumab/FLOT combination improved EFS among patients with any degree of pathological response and irrespective of lymph node status at surgery.

Regarding nodal staging, which was done in 800 patients, the percentage who achieved negative nodal status was higher in the durvalumab group (58.2%) vs the placebo group (44.8%). However, the improvement in EFS with durvalumab was comparable for node-negative (HR, 0.74) and node-positive (HR, 0.77) patients.

Lorenzen said that overall, the results provide a solid answer to the question, “Is it time to change practice?”

“I think MATTERHORN gives us the largest dataset and answers this question satisfactorily,” she said. Given that overall survival improved regardless of PD-L1 expression, she added, the combination of durvalumab and FLOT should be offered to “all our patient subgroups.”

The study was funded by AstraZeneca. Tabernero made numerous disclosures, including relationships with AstraZeneca, Boehringer Ingelheim, Chugai, and Daichii Sankyo. Lorenzen disclosed financial interests in or serving as an invited speaker for Servier, Lilly, MSD, and BMS.

A version of this article appeared on Medscape.com . 

Hi, everyone. I’m Dr Kenny Lin. I am a family physician and associate director of the Lancaster General Hospital Family Medicine Residency, and I blog at Common Sense Family Doctor.

The receding of the pandemic and the understandable desire to return to normalcy has made COVID-19 vaccines a lower priority for many of our patients. However, family physicians should keep in mind that from October 1, 2024, to September 6, 2025, COVID-19 was responsible for an estimated 3.2 to 4.6 million outpatient visits, 360,000 to 520,000 hospitalizations, and 42,000 to 60,000 deaths.

In a previous commentary, I discussed the worsening disconnect between the evidence supporting the effectiveness and safety of vaccinations and increasing reluctance of patients and parents to receive them, fueled by misinformation from federal health agencies and the packing of the Advisory Committee on Immunization Practices (ACIP) with vaccine skeptics. Since then, Secretary of Health and Human Services (HHS), Robert F. Kennedy, Jr, has fired Dr Susan Monarez, his handpicked director of the CDC. This caused three senior CDC officials to resign in protest and precipitated further turmoil at the embattled agency. 

The FDA has approved 3 updated COVID-19 vaccines targeted to currently circulating strains: an mRNA vaccine from Moderna (Spikevax) for those aged 6 months or older; an mRNA vaccine from Pfizer/BioNTech (Comirnaty) for those aged ≥ 5 years; and a protein subunit vaccine from Novavax (Nuvaxovid) for those aged ≥ 12 years. However, approvals restricting the scope of these approvals to certain high-risk groups, combined with the ACIP’s recent decision to not explicitly recommend them for any group, have complicated access for many patients.

Medical groups, including the American Academy of Pediatrics (AAP), the American Academy of Family Physicians (AAFP), and the American College of Obstetricians and Gynecologists (ACOG), have published their own recommendations (Table). Of note, in opposition to the FDA and ACIP, the AAP and AAFP strongly recommend routine vaccination for children aged 6 to 23 months because they have the highest risk for hospitalization. The AAFP and ACOG both recommend COVID-19 vaccination in pregnancy to protect the pregnant patient and provide passive antibody protection to their infants up to 6 months of age. The Vaccine Integrity Project’s review of 12 safety studies published since June 2024 found that mRNA vaccines were not associated with increases in any adverse maternal or infant outcomes and had a possible protective effect against preterm birth.

In my previous commentary, 70% of Medscape readers indicated that they would follow vaccination recommendations from AAP even if they differed from CDC guidance. Administering vaccines outside of FDA labeling indications (i.e., “off label”) typically requires a physician’s prescription, which will almost certainly reduce COVID-19 vaccine uptake in children and pregnant patients, given that most people received these shots in pharmacies during the 2024-25 season. CVS and Walgreens, the country’s two largest pharmacy chains, are requiring physician prescriptions or waiting for ACIP guidance to make the new vaccines available in many states. However, an increasing number of states have implemented executive orders or passed legislation to permit pharmacists to provide vaccines to anyone who wants them. For example, the Pennsylvania State Board of Pharmacy voted unanimously to issue guidance that would allow pharmacists to administer any vaccines recommended by AAFP, AAP, or ACOG.

Erosion of vaccine uptake could easily worsen the burden of illness for our patients and the health system. Navigating the unnecessarily complex landscape of COVID-19 vaccines will be challenging, but it remains worthwhile.
 

Kenneth W. Lin, MD, MPH, Associate Director, Department of Family Medicine, Lancaster General Hospital, Lancaster, Pennsylvania, has disclosed the following relevant financial relationships: Serve(d) as a director, officer, partner, employee, advisor, consultant, or trustee for: UpToDate; American Academy of Family Physicians; Archdiocese of Washington; Association of Prevention Teaching and Research.

A version of this article appeared on Medscape.com.

Hi, everyone. I’m Dr Kenny Lin. I am a family physician and associate director of the Lancaster General Hospital Family Medicine Residency, and I blog at Common Sense Family Doctor.

The receding of the pandemic and the understandable desire to return to normalcy has made COVID-19 vaccines a lower priority for many of our patients. However, family physicians should keep in mind that from October 1, 2024, to September 6, 2025, COVID-19 was responsible for an estimated 3.2 to 4.6 million outpatient visits, 360,000 to 520,000 hospitalizations, and 42,000 to 60,000 deaths.

In a previous commentary, I discussed the worsening disconnect between the evidence supporting the effectiveness and safety of vaccinations and increasing reluctance of patients and parents to receive them, fueled by misinformation from federal health agencies and the packing of the Advisory Committee on Immunization Practices (ACIP) with vaccine skeptics. Since then, Secretary of Health and Human Services (HHS), Robert F. Kennedy, Jr, has fired Dr Susan Monarez, his handpicked director of the CDC. This caused three senior CDC officials to resign in protest and precipitated further turmoil at the embattled agency. 

The FDA has approved 3 updated COVID-19 vaccines targeted to currently circulating strains: an mRNA vaccine from Moderna (Spikevax) for those aged 6 months or older; an mRNA vaccine from Pfizer/BioNTech (Comirnaty) for those aged ≥ 5 years; and a protein subunit vaccine from Novavax (Nuvaxovid) for those aged ≥ 12 years. However, approvals restricting the scope of these approvals to certain high-risk groups, combined with the ACIP’s recent decision to not explicitly recommend them for any group, have complicated access for many patients.

Medical groups, including the American Academy of Pediatrics (AAP), the American Academy of Family Physicians (AAFP), and the American College of Obstetricians and Gynecologists (ACOG), have published their own recommendations (Table). Of note, in opposition to the FDA and ACIP, the AAP and AAFP strongly recommend routine vaccination for children aged 6 to 23 months because they have the highest risk for hospitalization. The AAFP and ACOG both recommend COVID-19 vaccination in pregnancy to protect the pregnant patient and provide passive antibody protection to their infants up to 6 months of age. The Vaccine Integrity Project’s review of 12 safety studies published since June 2024 found that mRNA vaccines were not associated with increases in any adverse maternal or infant outcomes and had a possible protective effect against preterm birth.

In my previous commentary, 70% of Medscape readers indicated that they would follow vaccination recommendations from AAP even if they differed from CDC guidance. Administering vaccines outside of FDA labeling indications (i.e., “off label”) typically requires a physician’s prescription, which will almost certainly reduce COVID-19 vaccine uptake in children and pregnant patients, given that most people received these shots in pharmacies during the 2024-25 season. CVS and Walgreens, the country’s two largest pharmacy chains, are requiring physician prescriptions or waiting for ACIP guidance to make the new vaccines available in many states. However, an increasing number of states have implemented executive orders or passed legislation to permit pharmacists to provide vaccines to anyone who wants them. For example, the Pennsylvania State Board of Pharmacy voted unanimously to issue guidance that would allow pharmacists to administer any vaccines recommended by AAFP, AAP, or ACOG.

Erosion of vaccine uptake could easily worsen the burden of illness for our patients and the health system. Navigating the unnecessarily complex landscape of COVID-19 vaccines will be challenging, but it remains worthwhile.
 

Kenneth W. Lin, MD, MPH, Associate Director, Department of Family Medicine, Lancaster General Hospital, Lancaster, Pennsylvania, has disclosed the following relevant financial relationships: Serve(d) as a director, officer, partner, employee, advisor, consultant, or trustee for: UpToDate; American Academy of Family Physicians; Archdiocese of Washington; Association of Prevention Teaching and Research.

A version of this article appeared on Medscape.com.

Hi, everyone. I’m Dr Kenny Lin. I am a family physician and associate director of the Lancaster General Hospital Family Medicine Residency, and I blog at Common Sense Family Doctor.

The receding of the pandemic and the understandable desire to return to normalcy has made COVID-19 vaccines a lower priority for many of our patients. However, family physicians should keep in mind that from October 1, 2024, to September 6, 2025, COVID-19 was responsible for an estimated 3.2 to 4.6 million outpatient visits, 360,000 to 520,000 hospitalizations, and 42,000 to 60,000 deaths.

In a previous commentary, I discussed the worsening disconnect between the evidence supporting the effectiveness and safety of vaccinations and increasing reluctance of patients and parents to receive them, fueled by misinformation from federal health agencies and the packing of the Advisory Committee on Immunization Practices (ACIP) with vaccine skeptics. Since then, Secretary of Health and Human Services (HHS), Robert F. Kennedy, Jr, has fired Dr Susan Monarez, his handpicked director of the CDC. This caused three senior CDC officials to resign in protest and precipitated further turmoil at the embattled agency. 

The FDA has approved 3 updated COVID-19 vaccines targeted to currently circulating strains: an mRNA vaccine from Moderna (Spikevax) for those aged 6 months or older; an mRNA vaccine from Pfizer/BioNTech (Comirnaty) for those aged ≥ 5 years; and a protein subunit vaccine from Novavax (Nuvaxovid) for those aged ≥ 12 years. However, approvals restricting the scope of these approvals to certain high-risk groups, combined with the ACIP’s recent decision to not explicitly recommend them for any group, have complicated access for many patients.

Medical groups, including the American Academy of Pediatrics (AAP), the American Academy of Family Physicians (AAFP), and the American College of Obstetricians and Gynecologists (ACOG), have published their own recommendations (Table). Of note, in opposition to the FDA and ACIP, the AAP and AAFP strongly recommend routine vaccination for children aged 6 to 23 months because they have the highest risk for hospitalization. The AAFP and ACOG both recommend COVID-19 vaccination in pregnancy to protect the pregnant patient and provide passive antibody protection to their infants up to 6 months of age. The Vaccine Integrity Project’s review of 12 safety studies published since June 2024 found that mRNA vaccines were not associated with increases in any adverse maternal or infant outcomes and had a possible protective effect against preterm birth.

In my previous commentary, 70% of Medscape readers indicated that they would follow vaccination recommendations from AAP even if they differed from CDC guidance. Administering vaccines outside of FDA labeling indications (i.e., “off label”) typically requires a physician’s prescription, which will almost certainly reduce COVID-19 vaccine uptake in children and pregnant patients, given that most people received these shots in pharmacies during the 2024-25 season. CVS and Walgreens, the country’s two largest pharmacy chains, are requiring physician prescriptions or waiting for ACIP guidance to make the new vaccines available in many states. However, an increasing number of states have implemented executive orders or passed legislation to permit pharmacists to provide vaccines to anyone who wants them. For example, the Pennsylvania State Board of Pharmacy voted unanimously to issue guidance that would allow pharmacists to administer any vaccines recommended by AAFP, AAP, or ACOG.

Erosion of vaccine uptake could easily worsen the burden of illness for our patients and the health system. Navigating the unnecessarily complex landscape of COVID-19 vaccines will be challenging, but it remains worthwhile.
 

Kenneth W. Lin, MD, MPH, Associate Director, Department of Family Medicine, Lancaster General Hospital, Lancaster, Pennsylvania, has disclosed the following relevant financial relationships: Serve(d) as a director, officer, partner, employee, advisor, consultant, or trustee for: UpToDate; American Academy of Family Physicians; Archdiocese of Washington; Association of Prevention Teaching and Research.

A version of this article appeared on Medscape.com.

In this Practical AI column, we’ve explored everything from large language models to the nuances of trial matching, but one of the most immediate and impactful applications of AI is unfolding right now in breast imaging. For oncologists, this isn’t an abstract future — with new screening guidelines, dense-breast mandates, and a shrinking radiology workforce, it’s the imaging reports and patient questions landing in your clinic today.

Here is what oncologists need to know, and how to put it to work for their patients.

 

Why AI in Mammography Matters

More than 200 million women undergo breast cancer screening each year. In the US alone, 10% of the 40 million women screened annually require additional diagnostic imaging, and 4%–5% of these women are eventually diagnosed with breast cancer.

Two major shifts are redefining breast cancer screening in the US: The US Preventive Services Task Force (USPSTF) now recommends biennial screening from age 40 to 74 years, and notifying patients of breast density is a federal requirement as of September 10, 2024. That means more mammograms, more patient questions, and more downstream oncology decisions. Patients will increasingly ask about “dense” breast results and what to do next. Add a national radiologist shortage into the mix, and the pressure on timely callbacks, biopsies, and treatment planning will only grow.

 

Can AI Help Without Compromising Care?

The short answer is yes. With AI, we may be able to transform these rate-limiting steps into opportunities for earlier detection, decentralized screening, and smarter triage and save hundreds of thousands of women from an unnecessary diagnostic procedure, if implemented deliberately.

Don’t Confuse Today’s AI With Yesterday’s CAD 

Think of older computer-aided detection (CAD) like a 1990s chemotherapy drug: It sometimes helped, but it came with significant toxicity and rarely delivered consistent survival benefits. Today’s deep-learning AI is closer to targeted therapy — trained on millions of “trial participants” (mammograms), more precise, and applied in specific contexts where it adds value. If you once dismissed CAD as noise, it’s time to revisit what AI can now offer.

The role of AI is broader than drawing boxes. It provides second readings, worklist triage, risk prediction, density assessment, and decision support. FDA has cleared several AI tools for both 2D and digital breast tomosynthesis (DBT), which include iCAD ProFound (DBT), ScreenPoint Transpara (2D/DBT), and Lunit INSIGHT DBT. 

Some of the strongest evidence for AI in mammography is as a second reader during screening. Large trials show that AI plus one radiologist can match reading from two radiologists, cutting workload by about 40%. For example, the MASAI randomized trial showed that AI-supported screening achieved similar cancer detection but cut human screen-reading workload about 44% vs standard double reading (39,996 vs 40,024 participants). The primary interval cancer outcomes are maturing, but the safety analysis is reassuring.

Reducing second reads and arbitration time are important for clinicians because it frees capacity for callbacks and diagnostic workups. This will be especially key given that screening now starts at age 40. That will mean about 21 to 22 million more women are newly eligible, translating to about 10 to 11 million additional mammograms each year under biennial screening.

Another important area where AI can make its mark in mammography is triage and time to diagnosis. The results from a randomized implementation study showed that AI-prioritized worklists accelerated time to additional imaging and biopsy diagnosis without harming efficiency for others — exactly the kind of outcome patients feel.

Multiple studies have demonstrated improved diagnostic performance and shorter reading times when AI supports DBT interpretation, which is important because DBT can otherwise be time intensive.

We are also seeing rapid advancement in risk-based screening, moving beyond a single dense vs not dense approach. Deep-learning risk models, such as Mirai, predict 1- to 5-year breast cancer risk directly from the mammogram, and these tools are now being assessed prospectively to guide supplemental MRI. Cost-effectiveness modeling supports risk-stratified intervals vs one-size-fits-all schedules.

Finally, automated density tools, such as Transpara Density and Volpara, offer objective, reproducible volumetric measures that map to the Breast Imaging-Reporting and Data System, which is useful for Mammography Quality Standards Act-required reporting and as inputs to risk calculators.

While early evidence suggests AI may help surface future or interval cancers earlier, including more invasive tumors, the definitive impacts on interval cancer rates and mortality require longitudinal follow-up, which is now in progress.

 

Pitfalls to Watch For

Bias is real. Studies show false-positive differences by race, age, and density. AI can even infer racial identity from images, potentially amplifying disparities. Performance can also shift by vendor, demographics, and prevalence.

A Radiology study of 4855 DBT exams showed that an algorithm produced more false-positive case scores in Black patients and older patients (aged 71-80 years) patients and in women with extremely dense breasts. This can happen because AI can infer proxies for race directly from images, even when humans cannot, and this can propagate disparities if not addressed. External validations and reviews emphasize that performance can shift with device manufacturer, demographics, and prevalence, which is why all tools need to undergo local validation and calibration. 

Here’s a pragmatic adoption checklist before going live with an AI tool.

• Confirm FDA clearance: Verify the name and version of the algorithm, imaging modes (2D vs DBT), and operating points. Confirm 510(k) numbers.
• Local validation: Test on your patient mix and vendor stack (Hologic, GE, Siemens, Fuji). Compare this to your baseline recall rate, positive predictive value of recall (PPV1), cancer detection rate, and reading time. Commit to recalibration if drift occurs.
• Equity plan: Monitor false-positive and negative false-rates by age, race/ethnicity, and density; document corrective actions if disparities emerge. (This isn’t optional.)
• Workflow clarity: Is AI a second reader, an additional reader, or a triage tool? Who arbitrates discordance? What’s the escalation path for high-risk or interval cancer-like patterns?
• Regulatory strategy: Confirm whether the vendor has (or will file) a Predetermined Change Control Plan so models can be updated safely without repeated submissions. Also confirm how you’ll be notified about performance-relevant changes.
• Data governance: Audit logs of AI outputs, retention, protected health information handling, and the patient communication policy for AI-assisted reads.

After going live, set up a quarterly dashboard. It should include cancer detection rate per 1000 patients, recall rate, PPV1, interval cancer rate (as it matures), reading time, and turnaround time to diagnostic imaging or biopsy — all stratified by age, race/ethnicity, and density.

Here, I dissect what this discussion means through the lens of Moravec’s paradox (machines excel at what clinicians find hard, and vice versa) and offer a possible playbook for putting these tools to work.

 

What to Tell Patients

When speaking with patients, emphasize that a radiologist still reads their mammogram. AI helps with consistency and efficiency; it doesn’t replace human oversight. Patients with dense breasts should still expect a standard notice; discussion of individualized risk factors, such as family history, genetics, and prior biopsies; and consideration of supplemental imaging if risk warrants. But it’s also important to tell these patients that while dense breasts are common, they do not automatically mean high cancer risk.

As for screening schedules, remind patients that screening is at least biennial from 40 to 74 years of age per the USPSTF guidelines; however, specialty groups may recommend starting on an annual schedule at 40.

 

What You Can Implement Now

There are multiple practical use cases you can introduce now. One is to use AI as a second reader or an additional reader safety net to preserve detection while reducing human workload. This helps your breast center absorb screening expansion to age 40 without diluting quality. Another is to turn on AI triage to shorten the time to callback and biopsy for the few who need it most — patients notice and appreciate faster answers. You can also begin adopting automated density plus risk models to move beyond “dense/not dense.” For selected patients, AI-informed risk can justify MRI or tailored intervals. 

Here’s a quick cheat sheet (for your next leadership or tumor-board meeting).

 

Do:

• Use AI as a second or additional reader or triage tool, not as a black box.
• Track cancer detection rate, recall, PPV1, interval cancers, and reading time, stratified by age, race, and breast density.
• Pair automated density with AI risk to personalize screening and supplemental imaging.
• Enroll patients in future clinical trials, such as PRISM, the first large-scale randomized controlled trial of AI for screening mammography. This US-based, $16 million, seven-site study is funded by the Patient-Centered Outcomes Research Institute.

Don’t:

• Assume “AI = CAD.” The 2015 CAD story is over; modern deep learning systems are different and require different oversight.
• Go live without a local validation and equity plan or without clarity on software updates.
• Forget to remind patients that screening starts at age 40, and dense breast notifications are now universal. Use the visit to discuss risk, supplemental imaging, and why a human still directs their care.

The Bottom Line

AI won’t replace radiologists or read mammograms for us — just as PET scans didn’t replace oncologists and stethoscopes didn’t make cardiologists obsolete. What it will do is catch what the tired human eye might miss, shave days off anxious waiting, and turn breast density into data instead of doubt. For oncologists, that means staging sooner, enrolling smarter, and spending more time talking with patients instead of chasing callbacks.

In short, AI may not take the picture, but it helps us frame the story, making it sharper, faster, and with fewer blind spots. By pairing this powerful technology with rigorous, equity-focused local validation and transparent governance under the FDA’s emerging Predetermined Change Control Plan framework, we can realize the tangible benefits of practical AI for our patients without widening disparities. 

Now, during Breast Cancer Awareness Month, how about we add on AI to that pink ribbon — how cool would that be?

Thoughts? Drop me a line at [email protected]. Let’s keep the conversation — and pink ribbons — going.

Arturo Loaiza-Bonilla, MD, MSEd, is the co-founder and chief medical AI officer at Massive Bio, a company connecting patients to clinical trials using artificial intelligence. His research and professional interests focus on precision medicine, clinical trial design, digital health, entrepreneurship, and patient advocacy. Dr Loaiza-Bonilla serves as Systemwide Chief of Hematology and Oncology at St. Luke’s University Health Network, where he maintains a connection to patient care by attending to patients 2 days a week.

A version of this article first appeared on Medscape.com.

In this Practical AI column, we’ve explored everything from large language models to the nuances of trial matching, but one of the most immediate and impactful applications of AI is unfolding right now in breast imaging. For oncologists, this isn’t an abstract future — with new screening guidelines, dense-breast mandates, and a shrinking radiology workforce, it’s the imaging reports and patient questions landing in your clinic today.

Here is what oncologists need to know, and how to put it to work for their patients.

 

Why AI in Mammography Matters

More than 200 million women undergo breast cancer screening each year. In the US alone, 10% of the 40 million women screened annually require additional diagnostic imaging, and 4%–5% of these women are eventually diagnosed with breast cancer.

Two major shifts are redefining breast cancer screening in the US: The US Preventive Services Task Force (USPSTF) now recommends biennial screening from age 40 to 74 years, and notifying patients of breast density is a federal requirement as of September 10, 2024. That means more mammograms, more patient questions, and more downstream oncology decisions. Patients will increasingly ask about “dense” breast results and what to do next. Add a national radiologist shortage into the mix, and the pressure on timely callbacks, biopsies, and treatment planning will only grow.

 

Can AI Help Without Compromising Care?

The short answer is yes. With AI, we may be able to transform these rate-limiting steps into opportunities for earlier detection, decentralized screening, and smarter triage and save hundreds of thousands of women from an unnecessary diagnostic procedure, if implemented deliberately.

Don’t Confuse Today’s AI With Yesterday’s CAD 

Think of older computer-aided detection (CAD) like a 1990s chemotherapy drug: It sometimes helped, but it came with significant toxicity and rarely delivered consistent survival benefits. Today’s deep-learning AI is closer to targeted therapy — trained on millions of “trial participants” (mammograms), more precise, and applied in specific contexts where it adds value. If you once dismissed CAD as noise, it’s time to revisit what AI can now offer.

The role of AI is broader than drawing boxes. It provides second readings, worklist triage, risk prediction, density assessment, and decision support. FDA has cleared several AI tools for both 2D and digital breast tomosynthesis (DBT), which include iCAD ProFound (DBT), ScreenPoint Transpara (2D/DBT), and Lunit INSIGHT DBT. 

Some of the strongest evidence for AI in mammography is as a second reader during screening. Large trials show that AI plus one radiologist can match reading from two radiologists, cutting workload by about 40%. For example, the MASAI randomized trial showed that AI-supported screening achieved similar cancer detection but cut human screen-reading workload about 44% vs standard double reading (39,996 vs 40,024 participants). The primary interval cancer outcomes are maturing, but the safety analysis is reassuring.

Reducing second reads and arbitration time are important for clinicians because it frees capacity for callbacks and diagnostic workups. This will be especially key given that screening now starts at age 40. That will mean about 21 to 22 million more women are newly eligible, translating to about 10 to 11 million additional mammograms each year under biennial screening.

Another important area where AI can make its mark in mammography is triage and time to diagnosis. The results from a randomized implementation study showed that AI-prioritized worklists accelerated time to additional imaging and biopsy diagnosis without harming efficiency for others — exactly the kind of outcome patients feel.

Multiple studies have demonstrated improved diagnostic performance and shorter reading times when AI supports DBT interpretation, which is important because DBT can otherwise be time intensive.

We are also seeing rapid advancement in risk-based screening, moving beyond a single dense vs not dense approach. Deep-learning risk models, such as Mirai, predict 1- to 5-year breast cancer risk directly from the mammogram, and these tools are now being assessed prospectively to guide supplemental MRI. Cost-effectiveness modeling supports risk-stratified intervals vs one-size-fits-all schedules.

Finally, automated density tools, such as Transpara Density and Volpara, offer objective, reproducible volumetric measures that map to the Breast Imaging-Reporting and Data System, which is useful for Mammography Quality Standards Act-required reporting and as inputs to risk calculators.

While early evidence suggests AI may help surface future or interval cancers earlier, including more invasive tumors, the definitive impacts on interval cancer rates and mortality require longitudinal follow-up, which is now in progress.

 

Pitfalls to Watch For

Bias is real. Studies show false-positive differences by race, age, and density. AI can even infer racial identity from images, potentially amplifying disparities. Performance can also shift by vendor, demographics, and prevalence.

A Radiology study of 4855 DBT exams showed that an algorithm produced more false-positive case scores in Black patients and older patients (aged 71-80 years) patients and in women with extremely dense breasts. This can happen because AI can infer proxies for race directly from images, even when humans cannot, and this can propagate disparities if not addressed. External validations and reviews emphasize that performance can shift with device manufacturer, demographics, and prevalence, which is why all tools need to undergo local validation and calibration. 

Here’s a pragmatic adoption checklist before going live with an AI tool.

• Confirm FDA clearance: Verify the name and version of the algorithm, imaging modes (2D vs DBT), and operating points. Confirm 510(k) numbers.
• Local validation: Test on your patient mix and vendor stack (Hologic, GE, Siemens, Fuji). Compare this to your baseline recall rate, positive predictive value of recall (PPV1), cancer detection rate, and reading time. Commit to recalibration if drift occurs.
• Equity plan: Monitor false-positive and negative false-rates by age, race/ethnicity, and density; document corrective actions if disparities emerge. (This isn’t optional.)
• Workflow clarity: Is AI a second reader, an additional reader, or a triage tool? Who arbitrates discordance? What’s the escalation path for high-risk or interval cancer-like patterns?
• Regulatory strategy: Confirm whether the vendor has (or will file) a Predetermined Change Control Plan so models can be updated safely without repeated submissions. Also confirm how you’ll be notified about performance-relevant changes.
• Data governance: Audit logs of AI outputs, retention, protected health information handling, and the patient communication policy for AI-assisted reads.

After going live, set up a quarterly dashboard. It should include cancer detection rate per 1000 patients, recall rate, PPV1, interval cancer rate (as it matures), reading time, and turnaround time to diagnostic imaging or biopsy — all stratified by age, race/ethnicity, and density.

Here, I dissect what this discussion means through the lens of Moravec’s paradox (machines excel at what clinicians find hard, and vice versa) and offer a possible playbook for putting these tools to work.

 

What to Tell Patients

When speaking with patients, emphasize that a radiologist still reads their mammogram. AI helps with consistency and efficiency; it doesn’t replace human oversight. Patients with dense breasts should still expect a standard notice; discussion of individualized risk factors, such as family history, genetics, and prior biopsies; and consideration of supplemental imaging if risk warrants. But it’s also important to tell these patients that while dense breasts are common, they do not automatically mean high cancer risk.

As for screening schedules, remind patients that screening is at least biennial from 40 to 74 years of age per the USPSTF guidelines; however, specialty groups may recommend starting on an annual schedule at 40.

 

What You Can Implement Now

There are multiple practical use cases you can introduce now. One is to use AI as a second reader or an additional reader safety net to preserve detection while reducing human workload. This helps your breast center absorb screening expansion to age 40 without diluting quality. Another is to turn on AI triage to shorten the time to callback and biopsy for the few who need it most — patients notice and appreciate faster answers. You can also begin adopting automated density plus risk models to move beyond “dense/not dense.” For selected patients, AI-informed risk can justify MRI or tailored intervals. 

Here’s a quick cheat sheet (for your next leadership or tumor-board meeting).

 

Do:

• Use AI as a second or additional reader or triage tool, not as a black box.
• Track cancer detection rate, recall, PPV1, interval cancers, and reading time, stratified by age, race, and breast density.
• Pair automated density with AI risk to personalize screening and supplemental imaging.
• Enroll patients in future clinical trials, such as PRISM, the first large-scale randomized controlled trial of AI for screening mammography. This US-based, $16 million, seven-site study is funded by the Patient-Centered Outcomes Research Institute.

Don’t:

• Assume “AI = CAD.” The 2015 CAD story is over; modern deep learning systems are different and require different oversight.
• Go live without a local validation and equity plan or without clarity on software updates.
• Forget to remind patients that screening starts at age 40, and dense breast notifications are now universal. Use the visit to discuss risk, supplemental imaging, and why a human still directs their care.

The Bottom Line

AI won’t replace radiologists or read mammograms for us — just as PET scans didn’t replace oncologists and stethoscopes didn’t make cardiologists obsolete. What it will do is catch what the tired human eye might miss, shave days off anxious waiting, and turn breast density into data instead of doubt. For oncologists, that means staging sooner, enrolling smarter, and spending more time talking with patients instead of chasing callbacks.

In short, AI may not take the picture, but it helps us frame the story, making it sharper, faster, and with fewer blind spots. By pairing this powerful technology with rigorous, equity-focused local validation and transparent governance under the FDA’s emerging Predetermined Change Control Plan framework, we can realize the tangible benefits of practical AI for our patients without widening disparities. 

Now, during Breast Cancer Awareness Month, how about we add on AI to that pink ribbon — how cool would that be?

Thoughts? Drop me a line at [email protected]. Let’s keep the conversation — and pink ribbons — going.

Arturo Loaiza-Bonilla, MD, MSEd, is the co-founder and chief medical AI officer at Massive Bio, a company connecting patients to clinical trials using artificial intelligence. His research and professional interests focus on precision medicine, clinical trial design, digital health, entrepreneurship, and patient advocacy. Dr Loaiza-Bonilla serves as Systemwide Chief of Hematology and Oncology at St. Luke’s University Health Network, where he maintains a connection to patient care by attending to patients 2 days a week.

A version of this article first appeared on Medscape.com.

In this Practical AI column, we’ve explored everything from large language models to the nuances of trial matching, but one of the most immediate and impactful applications of AI is unfolding right now in breast imaging. For oncologists, this isn’t an abstract future — with new screening guidelines, dense-breast mandates, and a shrinking radiology workforce, it’s the imaging reports and patient questions landing in your clinic today.

Here is what oncologists need to know, and how to put it to work for their patients.

 

Why AI in Mammography Matters

More than 200 million women undergo breast cancer screening each year. In the US alone, 10% of the 40 million women screened annually require additional diagnostic imaging, and 4%–5% of these women are eventually diagnosed with breast cancer.

Two major shifts are redefining breast cancer screening in the US: The US Preventive Services Task Force (USPSTF) now recommends biennial screening from age 40 to 74 years, and notifying patients of breast density is a federal requirement as of September 10, 2024. That means more mammograms, more patient questions, and more downstream oncology decisions. Patients will increasingly ask about “dense” breast results and what to do next. Add a national radiologist shortage into the mix, and the pressure on timely callbacks, biopsies, and treatment planning will only grow.

 

Can AI Help Without Compromising Care?

The short answer is yes. With AI, we may be able to transform these rate-limiting steps into opportunities for earlier detection, decentralized screening, and smarter triage and save hundreds of thousands of women from an unnecessary diagnostic procedure, if implemented deliberately.

Don’t Confuse Today’s AI With Yesterday’s CAD 

Think of older computer-aided detection (CAD) like a 1990s chemotherapy drug: It sometimes helped, but it came with significant toxicity and rarely delivered consistent survival benefits. Today’s deep-learning AI is closer to targeted therapy — trained on millions of “trial participants” (mammograms), more precise, and applied in specific contexts where it adds value. If you once dismissed CAD as noise, it’s time to revisit what AI can now offer.

The role of AI is broader than drawing boxes. It provides second readings, worklist triage, risk prediction, density assessment, and decision support. FDA has cleared several AI tools for both 2D and digital breast tomosynthesis (DBT), which include iCAD ProFound (DBT), ScreenPoint Transpara (2D/DBT), and Lunit INSIGHT DBT. 

Some of the strongest evidence for AI in mammography is as a second reader during screening. Large trials show that AI plus one radiologist can match reading from two radiologists, cutting workload by about 40%. For example, the MASAI randomized trial showed that AI-supported screening achieved similar cancer detection but cut human screen-reading workload about 44% vs standard double reading (39,996 vs 40,024 participants). The primary interval cancer outcomes are maturing, but the safety analysis is reassuring.

Reducing second reads and arbitration time are important for clinicians because it frees capacity for callbacks and diagnostic workups. This will be especially key given that screening now starts at age 40. That will mean about 21 to 22 million more women are newly eligible, translating to about 10 to 11 million additional mammograms each year under biennial screening.

Another important area where AI can make its mark in mammography is triage and time to diagnosis. The results from a randomized implementation study showed that AI-prioritized worklists accelerated time to additional imaging and biopsy diagnosis without harming efficiency for others — exactly the kind of outcome patients feel.

Multiple studies have demonstrated improved diagnostic performance and shorter reading times when AI supports DBT interpretation, which is important because DBT can otherwise be time intensive.

We are also seeing rapid advancement in risk-based screening, moving beyond a single dense vs not dense approach. Deep-learning risk models, such as Mirai, predict 1- to 5-year breast cancer risk directly from the mammogram, and these tools are now being assessed prospectively to guide supplemental MRI. Cost-effectiveness modeling supports risk-stratified intervals vs one-size-fits-all schedules.

Finally, automated density tools, such as Transpara Density and Volpara, offer objective, reproducible volumetric measures that map to the Breast Imaging-Reporting and Data System, which is useful for Mammography Quality Standards Act-required reporting and as inputs to risk calculators.

While early evidence suggests AI may help surface future or interval cancers earlier, including more invasive tumors, the definitive impacts on interval cancer rates and mortality require longitudinal follow-up, which is now in progress.

 

Pitfalls to Watch For

Bias is real. Studies show false-positive differences by race, age, and density. AI can even infer racial identity from images, potentially amplifying disparities. Performance can also shift by vendor, demographics, and prevalence.

A Radiology study of 4855 DBT exams showed that an algorithm produced more false-positive case scores in Black patients and older patients (aged 71-80 years) patients and in women with extremely dense breasts. This can happen because AI can infer proxies for race directly from images, even when humans cannot, and this can propagate disparities if not addressed. External validations and reviews emphasize that performance can shift with device manufacturer, demographics, and prevalence, which is why all tools need to undergo local validation and calibration. 

Here’s a pragmatic adoption checklist before going live with an AI tool.

• Confirm FDA clearance: Verify the name and version of the algorithm, imaging modes (2D vs DBT), and operating points. Confirm 510(k) numbers.
• Local validation: Test on your patient mix and vendor stack (Hologic, GE, Siemens, Fuji). Compare this to your baseline recall rate, positive predictive value of recall (PPV1), cancer detection rate, and reading time. Commit to recalibration if drift occurs.
• Equity plan: Monitor false-positive and negative false-rates by age, race/ethnicity, and density; document corrective actions if disparities emerge. (This isn’t optional.)
• Workflow clarity: Is AI a second reader, an additional reader, or a triage tool? Who arbitrates discordance? What’s the escalation path for high-risk or interval cancer-like patterns?
• Regulatory strategy: Confirm whether the vendor has (or will file) a Predetermined Change Control Plan so models can be updated safely without repeated submissions. Also confirm how you’ll be notified about performance-relevant changes.
• Data governance: Audit logs of AI outputs, retention, protected health information handling, and the patient communication policy for AI-assisted reads.

After going live, set up a quarterly dashboard. It should include cancer detection rate per 1000 patients, recall rate, PPV1, interval cancer rate (as it matures), reading time, and turnaround time to diagnostic imaging or biopsy — all stratified by age, race/ethnicity, and density.

Here, I dissect what this discussion means through the lens of Moravec’s paradox (machines excel at what clinicians find hard, and vice versa) and offer a possible playbook for putting these tools to work.

 

What to Tell Patients

When speaking with patients, emphasize that a radiologist still reads their mammogram. AI helps with consistency and efficiency; it doesn’t replace human oversight. Patients with dense breasts should still expect a standard notice; discussion of individualized risk factors, such as family history, genetics, and prior biopsies; and consideration of supplemental imaging if risk warrants. But it’s also important to tell these patients that while dense breasts are common, they do not automatically mean high cancer risk.

As for screening schedules, remind patients that screening is at least biennial from 40 to 74 years of age per the USPSTF guidelines; however, specialty groups may recommend starting on an annual schedule at 40.

 

What You Can Implement Now

There are multiple practical use cases you can introduce now. One is to use AI as a second reader or an additional reader safety net to preserve detection while reducing human workload. This helps your breast center absorb screening expansion to age 40 without diluting quality. Another is to turn on AI triage to shorten the time to callback and biopsy for the few who need it most — patients notice and appreciate faster answers. You can also begin adopting automated density plus risk models to move beyond “dense/not dense.” For selected patients, AI-informed risk can justify MRI or tailored intervals. 

Here’s a quick cheat sheet (for your next leadership or tumor-board meeting).

 

Do:

• Use AI as a second or additional reader or triage tool, not as a black box.
• Track cancer detection rate, recall, PPV1, interval cancers, and reading time, stratified by age, race, and breast density.
• Pair automated density with AI risk to personalize screening and supplemental imaging.
• Enroll patients in future clinical trials, such as PRISM, the first large-scale randomized controlled trial of AI for screening mammography. This US-based, $16 million, seven-site study is funded by the Patient-Centered Outcomes Research Institute.

Don’t:

• Assume “AI = CAD.” The 2015 CAD story is over; modern deep learning systems are different and require different oversight.
• Go live without a local validation and equity plan or without clarity on software updates.
• Forget to remind patients that screening starts at age 40, and dense breast notifications are now universal. Use the visit to discuss risk, supplemental imaging, and why a human still directs their care.

The Bottom Line

AI won’t replace radiologists or read mammograms for us — just as PET scans didn’t replace oncologists and stethoscopes didn’t make cardiologists obsolete. What it will do is catch what the tired human eye might miss, shave days off anxious waiting, and turn breast density into data instead of doubt. For oncologists, that means staging sooner, enrolling smarter, and spending more time talking with patients instead of chasing callbacks.

In short, AI may not take the picture, but it helps us frame the story, making it sharper, faster, and with fewer blind spots. By pairing this powerful technology with rigorous, equity-focused local validation and transparent governance under the FDA’s emerging Predetermined Change Control Plan framework, we can realize the tangible benefits of practical AI for our patients without widening disparities. 

Now, during Breast Cancer Awareness Month, how about we add on AI to that pink ribbon — how cool would that be?

Thoughts? Drop me a line at [email protected]. Let’s keep the conversation — and pink ribbons — going.

Arturo Loaiza-Bonilla, MD, MSEd, is the co-founder and chief medical AI officer at Massive Bio, a company connecting patients to clinical trials using artificial intelligence. His research and professional interests focus on precision medicine, clinical trial design, digital health, entrepreneurship, and patient advocacy. Dr Loaiza-Bonilla serves as Systemwide Chief of Hematology and Oncology at St. Luke’s University Health Network, where he maintains a connection to patient care by attending to patients 2 days a week.

A version of this article first appeared on Medscape.com.