{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T01:39:10Z","timestamp":1784165950602,"version":"3.55.0"},"reference-count":43,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T00:00:00Z","timestamp":1763856000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T00:00:00Z","timestamp":1763856000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["accpjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pharmacotherapy"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The treatment of migraine is hampered by inter\u2010individual variability, leading to an inefficient \u201ctrial and error\u201d approach. Artificial intelligence (AI) and machine learning (ML) offer a path towards precision medicine by predicting therapeutic outcomes. This scoping review systematically evaluates the evidence for AI and ML models for predicting pharmacologic response in migraine. A systematic search of four databases (PubMed, Web of Knowledge, Cochrane Library, and OpenGrey) identified 12 eligible studies using AI\/ML to predict acute or prophylactic response to migraine treatment. These studies, which date back to articles published in 2006 and have been increasingly published recently, used a wide range of methods, from classical algorithms like support vector machines to deep learning and probabilistic models. The models primarily utilized clinical phenotyping and neuroimaging data and reported high predictive accuracy for novel biologics (e.g., anti\u2010calcitonin gene\u2010related peptide monoclonal antibodies (CGRP mAbs)) and acute treatments (e.g., nonsteroidal anti\u2010inflammatory drugs (NSAIDs)). However, our systematic review finds that this apparent success is undermined by critical and pervasive methodological weaknesses. The central finding is that most studies relied solely on internal validation, carrying a high risk of overfitting, with external validation being exceptionally rare. Furthermore, several publications were based on overlapping patient cohorts, and a complete lack of biomarker or genetic data was noted. Consequently, the clinical application of AI and ML is currently stalled. Future progress depends on overcoming the \u201ccrisis of generalizability\u201d by mandating external validation, addressing the \u201cdata bottleneck\u201d with large, diverse datasets, and expanding data modalities to include \u201comic\u201d data. These measures are critical to begin to realize the potential of AI and ML to personalize migraine treatment and significantly improve patient outcomes.<\/jats:p>","DOI":"10.1002\/phar.70085","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T03:38:28Z","timestamp":1763955508000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting Pharmacological Treatment Response in Migraine Using\n                    <scp>AI<\/scp>\n                    \/\n                    <scp>ML<\/scp>\n                    : A Scoping Review of the Evidence and Future Directions"],"prefix":"10.1002","volume":"46","author":[{"given":"Martina","family":"Giacon","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical Sciences University of Piemonte Orientale  Novara Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4909-1121","authenticated-orcid":false,"given":"Salvatore","family":"Terrazzino","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Sciences University of Piemonte Orientale  Novara 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