MD; Alan Rapoport

As we move further into the 21st century, technology continues to revolutionize various facets of our lives. Healthcare is a prime example. Advances in technology have dramatically reshaped the way we develop medications, diagnose diseases, and enhance patient care. The rise of artificial intelligence (AI) and the widespread adoption of digital health technologies have marked a significant milestone in improving the quality of care. AI, with its ability to leverage algorithms, deep learning, and machine learning to process data, make decisions, and perform tasks autonomously, is becoming an integral part of modern society. It is embedded in various technologies that we rely on daily, from smartphones and smart home devices to content recommendations on streaming services and social media platforms.

In healthcare, AI has applications in numerous fields, such as radiology. AI streamlines processes such as organizing patient appointments, optimizing radiation protocols for safety and efficiency, and enhancing the documentation process through advanced image analysis. AI technology plays an integral role in imaging tasks like image enhancement, lesion detection, and precise measurement. In difficult-to-interpret radiologic studies, such as some mammography images, it can be a crucial aid to the radiologist. Additionally, the use of AI has significantly improved remote patient monitoring that enables healthcare professionals to monitor and assess patient conditions without needing in-person visits. Remote patient monitoring gained prominence during the COVID-19 pandemic and continues to be a valuable tool in post pandemic care. Study results have highlighted that AI-driven ambient dictation tools have increased provider engagement with patients during consultations while reducing the time spent documenting in electronic health records.

Like many other medical specialties, headache medicine also uses AI. Most prominently, AI has been used in models and engines in assisting with headache diagnoses. A noteworthy example of AI in headache medicine is the development of an online, computer-based diagnostic engine (CDE) by Rapoport et al, called BonTriage. This tool is designed to diagnose headaches by employing a rule set based on the International Classification of Headache Disorders-3 (ICHD-3) criteria for primary headache disorders while also evaluating secondary headaches and medication overuse headaches. By leveraging machine learning, the CDE has the potential to streamline the diagnostic process, reducing the number of questions needed to reach a diagnosis and making the experience more efficient. This information can then be printed as a PDF file and taken by the patient to a healthcare professional for further discussion, fostering a more accurate, fluid, and conversational consultation.

A study was conducted to evaluate the accuracy of the CDE. Participants were randomly assigned to 1 of 2 sequences: (1) using the CDE followed by a structured standard interview with a headache specialist using the same ICHD-3 criteria or (2) starting with the structured standard interview followed by the CDE. The results demonstrated nearly perfect agreement in diagnosing migraine and probable migraine between the CDE and structured standard interview (κ = 0.82, 95% CI: 0.74, 0.90). The CDE demonstrated a diagnostic accuracy of 91.6% (95% CI: 86.9%, 95.0%), a sensitivity rate of 89.0% (95% CI: 82.5%, 93.7%), and a specificity rate of 97.0% (95% CI: 89.5%, 99.6%).

A diagnostic engine such as this can save time that clinicians spend on documentation and allow more time for discussion with the patient. For instance, a patient can take the printout received from the CDE to an appointment; the printout gives a detailed history plus information about social and psychological issues, a list of medications taken, and results of previous testing. The CDE system was originally designed to help patients see a specialist in the environment of a nationwide lack of headache specialists. There are currently 45 million patients with headaches who are seeking treatment with only around 550 certified headache specialists in the United States. The CDE printed information can help a patient obtain a consultation from a clinician quickly and start evaluation and treatment earlier. This expert online consultation is currently free of charge.

Kwon et al developed a machine learning–based model designed to automatically classify headache disorders using data from a questionnaire. Their model was able to predict diagnoses for conditions such as migraine, tension-type headaches, trigeminal autonomic cephalalgia, epicranial headache, and thunderclap headaches. The model was trained on data from 2162 patients, all diagnosed by headache specialists, and achieved an overall accuracy of 81%, with a sensitivity of 88% and a specificity of 95% for diagnosing migraines. However, the model’s performance was less robust when applied to other headache disorders.

Katsuki et al developed an AI model to help non specialists accurately diagnose headaches. This model analyzed 17 variables and was trained on data from 2800 patients, with additional testing and refinement using data from another 200 patients. To evaluate its effectiveness, 2 groups of non-headache specialists each assessed 50 patients: 1 group relied solely on their expertise, while the other used the AI model. The group without AI assistance achieved an overall accuracy of 46% (κ = 0.21), while the group using the AI model significantly improved, reaching an overall accuracy of 83.2% (κ = 0.68).

Building on their work with AI for diagnosing headaches, Katsuki et al conducted a study using a smartphone application that tracked user-reported headache events alongside local weather data. The AI model revealed that lower barometric pressure, higher humidity, and increased rainfall were linked to the onset of headache attacks. The application also identified triggers for headaches in specific weather patterns, such as a drop in barometric pressure noted 6 hours before headache onset. The application of AI in monitoring weather changes could be crucial, especially given concerns that the rising frequency of severe weather events due to climate change may be exacerbating the severity and burden of migraine. Additionally, recent post hoc analyses of fremanezumab clinical trials have provided further evidence that weather changes can trigger headaches.

Rapoport and colleagues have also developed an application called Migraine Mentor, which accurately tracks headaches, triggers, health data, and response to medication on a smartphone. The patient spends 3 minutes a day answering a few questions about their day and whether they had a headache or took any medication. At 1 or 2 months, Migraine Mentor can generate a detailed report with data and current trends that is sent to the patient, which the patient can then share with the clinician. The application also reminds patients when to document data and take medication.

However, although the use of AI in headache medicine appears promising, caution must be exercised to ensure proper results and information are disseminated. One rapidly expanding application of AI is the widely popular ChatGPT. ChatGPT, which stands for generative pretraining transformer, is a type of large language model (LLM). An LLM is a deep learning algorithm designed to recognize, translate, predict, summarize, and generate text responses based on a given prompt. This model is trained on an extensive dataset that includes a diverse array of books, articles, and websites, exposing it to various language structures and styles. This training enables ChatGPT to generate responses that closely mimic human communication. LLMs are being used more and more in medicine to assist with generating patient documentation and educational materials.

However, Dr Fred Cohen published a perspective piece detailing how LLMs (such as ChatGPT) can produce misleading and inaccurate answers. In his example, he tasked ChatGPT to describe the epidemiology of migraines in penguins; the AI model generated a well-written and highly believable manuscript titled, “Migraine Under the Ice: Understanding Headaches in Antarctica's Feathered Friends.” The manuscript highlights that migraines are more prevalent in male penguins compared to females, with the peak age of onset occurring between 4 and 5 years. Additionally, emperor and king penguins are identified as being more susceptible to developing migraines compared to other penguin species. The paper was fictitious (as no studies on migraine in penguins have been written to date), exemplifying that these models can produce nonfactual materials.

For years, technological advancements have been reshaping many aspects of life, and medicine is no exception. AI has been successfully applied to streamline medical documentation, develop new drug targets, and deepen our understanding of various diseases. The field of headache medicine now also uses AI. Recent developments show significant promise, with AI aiding in the diagnosis of migraine and other headache disorders. AI models have even been used in the identification of potential drug targets for migraine treatment. Although there are still limitations to overcome, the future of AI in headache medicine appears bright.

If you would like to read more about Dr. Cohen’s work on AI and migraine, please visit fredcohenmd.com or TikTok @fredcohenmd. 

As we move further into the 21st century, technology continues to revolutionize various facets of our lives. Healthcare is a prime example. Advances in technology have dramatically reshaped the way we develop medications, diagnose diseases, and enhance patient care. The rise of artificial intelligence (AI) and the widespread adoption of digital health technologies have marked a significant milestone in improving the quality of care. AI, with its ability to leverage algorithms, deep learning, and machine learning to process data, make decisions, and perform tasks autonomously, is becoming an integral part of modern society. It is embedded in various technologies that we rely on daily, from smartphones and smart home devices to content recommendations on streaming services and social media platforms.

In healthcare, AI has applications in numerous fields, such as radiology. AI streamlines processes such as organizing patient appointments, optimizing radiation protocols for safety and efficiency, and enhancing the documentation process through advanced image analysis. AI technology plays an integral role in imaging tasks like image enhancement, lesion detection, and precise measurement. In difficult-to-interpret radiologic studies, such as some mammography images, it can be a crucial aid to the radiologist. Additionally, the use of AI has significantly improved remote patient monitoring that enables healthcare professionals to monitor and assess patient conditions without needing in-person visits. Remote patient monitoring gained prominence during the COVID-19 pandemic and continues to be a valuable tool in post pandemic care. Study results have highlighted that AI-driven ambient dictation tools have increased provider engagement with patients during consultations while reducing the time spent documenting in electronic health records.

Like many other medical specialties, headache medicine also uses AI. Most prominently, AI has been used in models and engines in assisting with headache diagnoses. A noteworthy example of AI in headache medicine is the development of an online, computer-based diagnostic engine (CDE) by Rapoport et al, called BonTriage. This tool is designed to diagnose headaches by employing a rule set based on the International Classification of Headache Disorders-3 (ICHD-3) criteria for primary headache disorders while also evaluating secondary headaches and medication overuse headaches. By leveraging machine learning, the CDE has the potential to streamline the diagnostic process, reducing the number of questions needed to reach a diagnosis and making the experience more efficient. This information can then be printed as a PDF file and taken by the patient to a healthcare professional for further discussion, fostering a more accurate, fluid, and conversational consultation.

A study was conducted to evaluate the accuracy of the CDE. Participants were randomly assigned to 1 of 2 sequences: (1) using the CDE followed by a structured standard interview with a headache specialist using the same ICHD-3 criteria or (2) starting with the structured standard interview followed by the CDE. The results demonstrated nearly perfect agreement in diagnosing migraine and probable migraine between the CDE and structured standard interview (κ = 0.82, 95% CI: 0.74, 0.90). The CDE demonstrated a diagnostic accuracy of 91.6% (95% CI: 86.9%, 95.0%), a sensitivity rate of 89.0% (95% CI: 82.5%, 93.7%), and a specificity rate of 97.0% (95% CI: 89.5%, 99.6%).

A diagnostic engine such as this can save time that clinicians spend on documentation and allow more time for discussion with the patient. For instance, a patient can take the printout received from the CDE to an appointment; the printout gives a detailed history plus information about social and psychological issues, a list of medications taken, and results of previous testing. The CDE system was originally designed to help patients see a specialist in the environment of a nationwide lack of headache specialists. There are currently 45 million patients with headaches who are seeking treatment with only around 550 certified headache specialists in the United States. The CDE printed information can help a patient obtain a consultation from a clinician quickly and start evaluation and treatment earlier. This expert online consultation is currently free of charge.

Kwon et al developed a machine learning–based model designed to automatically classify headache disorders using data from a questionnaire. Their model was able to predict diagnoses for conditions such as migraine, tension-type headaches, trigeminal autonomic cephalalgia, epicranial headache, and thunderclap headaches. The model was trained on data from 2162 patients, all diagnosed by headache specialists, and achieved an overall accuracy of 81%, with a sensitivity of 88% and a specificity of 95% for diagnosing migraines. However, the model’s performance was less robust when applied to other headache disorders.

Katsuki et al developed an AI model to help non specialists accurately diagnose headaches. This model analyzed 17 variables and was trained on data from 2800 patients, with additional testing and refinement using data from another 200 patients. To evaluate its effectiveness, 2 groups of non-headache specialists each assessed 50 patients: 1 group relied solely on their expertise, while the other used the AI model. The group without AI assistance achieved an overall accuracy of 46% (κ = 0.21), while the group using the AI model significantly improved, reaching an overall accuracy of 83.2% (κ = 0.68).

Building on their work with AI for diagnosing headaches, Katsuki et al conducted a study using a smartphone application that tracked user-reported headache events alongside local weather data. The AI model revealed that lower barometric pressure, higher humidity, and increased rainfall were linked to the onset of headache attacks. The application also identified triggers for headaches in specific weather patterns, such as a drop in barometric pressure noted 6 hours before headache onset. The application of AI in monitoring weather changes could be crucial, especially given concerns that the rising frequency of severe weather events due to climate change may be exacerbating the severity and burden of migraine. Additionally, recent post hoc analyses of fremanezumab clinical trials have provided further evidence that weather changes can trigger headaches.

Rapoport and colleagues have also developed an application called Migraine Mentor, which accurately tracks headaches, triggers, health data, and response to medication on a smartphone. The patient spends 3 minutes a day answering a few questions about their day and whether they had a headache or took any medication. At 1 or 2 months, Migraine Mentor can generate a detailed report with data and current trends that is sent to the patient, which the patient can then share with the clinician. The application also reminds patients when to document data and take medication.

However, although the use of AI in headache medicine appears promising, caution must be exercised to ensure proper results and information are disseminated. One rapidly expanding application of AI is the widely popular ChatGPT. ChatGPT, which stands for generative pretraining transformer, is a type of large language model (LLM). An LLM is a deep learning algorithm designed to recognize, translate, predict, summarize, and generate text responses based on a given prompt. This model is trained on an extensive dataset that includes a diverse array of books, articles, and websites, exposing it to various language structures and styles. This training enables ChatGPT to generate responses that closely mimic human communication. LLMs are being used more and more in medicine to assist with generating patient documentation and educational materials.

However, Dr Fred Cohen published a perspective piece detailing how LLMs (such as ChatGPT) can produce misleading and inaccurate answers. In his example, he tasked ChatGPT to describe the epidemiology of migraines in penguins; the AI model generated a well-written and highly believable manuscript titled, “Migraine Under the Ice: Understanding Headaches in Antarctica's Feathered Friends.” The manuscript highlights that migraines are more prevalent in male penguins compared to females, with the peak age of onset occurring between 4 and 5 years. Additionally, emperor and king penguins are identified as being more susceptible to developing migraines compared to other penguin species. The paper was fictitious (as no studies on migraine in penguins have been written to date), exemplifying that these models can produce nonfactual materials.

For years, technological advancements have been reshaping many aspects of life, and medicine is no exception. AI has been successfully applied to streamline medical documentation, develop new drug targets, and deepen our understanding of various diseases. The field of headache medicine now also uses AI. Recent developments show significant promise, with AI aiding in the diagnosis of migraine and other headache disorders. AI models have even been used in the identification of potential drug targets for migraine treatment. Although there are still limitations to overcome, the future of AI in headache medicine appears bright.

If you would like to read more about Dr. Cohen’s work on AI and migraine, please visit fredcohenmd.com or TikTok @fredcohenmd. 

As we move further into the 21st century, technology continues to revolutionize various facets of our lives. Healthcare is a prime example. Advances in technology have dramatically reshaped the way we develop medications, diagnose diseases, and enhance patient care. The rise of artificial intelligence (AI) and the widespread adoption of digital health technologies have marked a significant milestone in improving the quality of care. AI, with its ability to leverage algorithms, deep learning, and machine learning to process data, make decisions, and perform tasks autonomously, is becoming an integral part of modern society. It is embedded in various technologies that we rely on daily, from smartphones and smart home devices to content recommendations on streaming services and social media platforms.

In healthcare, AI has applications in numerous fields, such as radiology. AI streamlines processes such as organizing patient appointments, optimizing radiation protocols for safety and efficiency, and enhancing the documentation process through advanced image analysis. AI technology plays an integral role in imaging tasks like image enhancement, lesion detection, and precise measurement. In difficult-to-interpret radiologic studies, such as some mammography images, it can be a crucial aid to the radiologist. Additionally, the use of AI has significantly improved remote patient monitoring that enables healthcare professionals to monitor and assess patient conditions without needing in-person visits. Remote patient monitoring gained prominence during the COVID-19 pandemic and continues to be a valuable tool in post pandemic care. Study results have highlighted that AI-driven ambient dictation tools have increased provider engagement with patients during consultations while reducing the time spent documenting in electronic health records.

Like many other medical specialties, headache medicine also uses AI. Most prominently, AI has been used in models and engines in assisting with headache diagnoses. A noteworthy example of AI in headache medicine is the development of an online, computer-based diagnostic engine (CDE) by Rapoport et al, called BonTriage. This tool is designed to diagnose headaches by employing a rule set based on the International Classification of Headache Disorders-3 (ICHD-3) criteria for primary headache disorders while also evaluating secondary headaches and medication overuse headaches. By leveraging machine learning, the CDE has the potential to streamline the diagnostic process, reducing the number of questions needed to reach a diagnosis and making the experience more efficient. This information can then be printed as a PDF file and taken by the patient to a healthcare professional for further discussion, fostering a more accurate, fluid, and conversational consultation.

A study was conducted to evaluate the accuracy of the CDE. Participants were randomly assigned to 1 of 2 sequences: (1) using the CDE followed by a structured standard interview with a headache specialist using the same ICHD-3 criteria or (2) starting with the structured standard interview followed by the CDE. The results demonstrated nearly perfect agreement in diagnosing migraine and probable migraine between the CDE and structured standard interview (κ = 0.82, 95% CI: 0.74, 0.90). The CDE demonstrated a diagnostic accuracy of 91.6% (95% CI: 86.9%, 95.0%), a sensitivity rate of 89.0% (95% CI: 82.5%, 93.7%), and a specificity rate of 97.0% (95% CI: 89.5%, 99.6%).

A diagnostic engine such as this can save time that clinicians spend on documentation and allow more time for discussion with the patient. For instance, a patient can take the printout received from the CDE to an appointment; the printout gives a detailed history plus information about social and psychological issues, a list of medications taken, and results of previous testing. The CDE system was originally designed to help patients see a specialist in the environment of a nationwide lack of headache specialists. There are currently 45 million patients with headaches who are seeking treatment with only around 550 certified headache specialists in the United States. The CDE printed information can help a patient obtain a consultation from a clinician quickly and start evaluation and treatment earlier. This expert online consultation is currently free of charge.

Kwon et al developed a machine learning–based model designed to automatically classify headache disorders using data from a questionnaire. Their model was able to predict diagnoses for conditions such as migraine, tension-type headaches, trigeminal autonomic cephalalgia, epicranial headache, and thunderclap headaches. The model was trained on data from 2162 patients, all diagnosed by headache specialists, and achieved an overall accuracy of 81%, with a sensitivity of 88% and a specificity of 95% for diagnosing migraines. However, the model’s performance was less robust when applied to other headache disorders.

Katsuki et al developed an AI model to help non specialists accurately diagnose headaches. This model analyzed 17 variables and was trained on data from 2800 patients, with additional testing and refinement using data from another 200 patients. To evaluate its effectiveness, 2 groups of non-headache specialists each assessed 50 patients: 1 group relied solely on their expertise, while the other used the AI model. The group without AI assistance achieved an overall accuracy of 46% (κ = 0.21), while the group using the AI model significantly improved, reaching an overall accuracy of 83.2% (κ = 0.68).

Building on their work with AI for diagnosing headaches, Katsuki et al conducted a study using a smartphone application that tracked user-reported headache events alongside local weather data. The AI model revealed that lower barometric pressure, higher humidity, and increased rainfall were linked to the onset of headache attacks. The application also identified triggers for headaches in specific weather patterns, such as a drop in barometric pressure noted 6 hours before headache onset. The application of AI in monitoring weather changes could be crucial, especially given concerns that the rising frequency of severe weather events due to climate change may be exacerbating the severity and burden of migraine. Additionally, recent post hoc analyses of fremanezumab clinical trials have provided further evidence that weather changes can trigger headaches.

Rapoport and colleagues have also developed an application called Migraine Mentor, which accurately tracks headaches, triggers, health data, and response to medication on a smartphone. The patient spends 3 minutes a day answering a few questions about their day and whether they had a headache or took any medication. At 1 or 2 months, Migraine Mentor can generate a detailed report with data and current trends that is sent to the patient, which the patient can then share with the clinician. The application also reminds patients when to document data and take medication.

However, although the use of AI in headache medicine appears promising, caution must be exercised to ensure proper results and information are disseminated. One rapidly expanding application of AI is the widely popular ChatGPT. ChatGPT, which stands for generative pretraining transformer, is a type of large language model (LLM). An LLM is a deep learning algorithm designed to recognize, translate, predict, summarize, and generate text responses based on a given prompt. This model is trained on an extensive dataset that includes a diverse array of books, articles, and websites, exposing it to various language structures and styles. This training enables ChatGPT to generate responses that closely mimic human communication. LLMs are being used more and more in medicine to assist with generating patient documentation and educational materials.

However, Dr Fred Cohen published a perspective piece detailing how LLMs (such as ChatGPT) can produce misleading and inaccurate answers. In his example, he tasked ChatGPT to describe the epidemiology of migraines in penguins; the AI model generated a well-written and highly believable manuscript titled, “Migraine Under the Ice: Understanding Headaches in Antarctica's Feathered Friends.” The manuscript highlights that migraines are more prevalent in male penguins compared to females, with the peak age of onset occurring between 4 and 5 years. Additionally, emperor and king penguins are identified as being more susceptible to developing migraines compared to other penguin species. The paper was fictitious (as no studies on migraine in penguins have been written to date), exemplifying that these models can produce nonfactual materials.

For years, technological advancements have been reshaping many aspects of life, and medicine is no exception. AI has been successfully applied to streamline medical documentation, develop new drug targets, and deepen our understanding of various diseases. The field of headache medicine now also uses AI. Recent developments show significant promise, with AI aiding in the diagnosis of migraine and other headache disorders. AI models have even been used in the identification of potential drug targets for migraine treatment. Although there are still limitations to overcome, the future of AI in headache medicine appears bright.

If you would like to read more about Dr. Cohen’s work on AI and migraine, please visit fredcohenmd.com or TikTok @fredcohenmd.