Comprehensive Summary
This systematic review examined the role of artificial intelligence in headache medicine by analyzing 76 studies that were published between 2000 and 2025. The review highlighted AI applications in diagnosis and subtype classification, prediction of migraine attacks, analysis of imaging and neurophysiological data, and treatment personalization. AI tools like supervised and deep learning models were able to achieve diagnostic accuracies between 70-98% with area under the curve (AUC) values up to 0.95 in externally validated studies. Specific use-cases included identifying migraine versus tension-type or cluster headaches, predicting attack onset using wearable and app-based data, and categorizing patients as at risk for medication overuse headaches. The c-statistics were about 0.83. Additionally, newer technologies like digital twins, wearable biomarker monitoring, synthetic data, and VR/AR with biofeedback were discussed in this review. But the review emphasized that despite the advances in technology, there were challenges like small sample sizes, limited external validation, data bias, privacy concerns, and interpretability issues in the review. They also touched upon the possibility of depersonalizing care.
Outcomes and Implications
The results of this review indicate that AI could aid in clinical decision-making for headache treatment by providing earlier diagnoses and more accurate and customized treatment regimes. The implications of this technology are that AI models would be able to help physicians rapidly diagnose primary from secondary headaches as well as personalize treatments in emergency situations. The authors emphasized, though, that in order to implement this technology, testing must be done in more diverse populations and according to regulatory guidelines. They also discussed how AI should only be used with clinicians and not as a method for replacing them, so that empathy and trust remain at the center of patient care. If properly developed, this technology can help address the underdiagnosis and undertreatment in headache medicine.