# Predicting Pharmacological Treatment Response in Migraine Using AI/ML: A Scoping Review of the Evidence and Future Directions

## TL;DR

This paper reviews how AI and machine learning can predict migraine treatment responses but finds most studies lack strong validation and diverse data.

## Contribution

The novel contribution is a systematic evaluation of AI/ML models for migraine treatment prediction, highlighting critical methodological gaps.

## Key findings

- Most AI/ML studies on migraine treatment prediction use internal validation, risking overfitting.
- Current models rely on clinical and neuroimaging data but lack biomarker or genetic information.
- External validation and diverse datasets are needed to improve generalizability and clinical application.

## Abstract

The treatment of migraine is hampered by inter‐individual variability, leading to an inefficient “trial and error” 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‐calcitonin gene‐related peptide monoclonal antibodies (CGRP mAbs)) and acute treatments (e.g., nonsteroidal anti‐inflammatory 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 “crisis of generalizability” by mandating external validation, addressing the “data bottleneck” with large, diverse datasets, and expanding data modalities to include “omic” data. These measures are critical to begin to realize the potential of AI and ML to personalize migraine treatment and significantly improve patient outcomes.