SynthesisJournal of neurology2026
'Man vs. Machine: can ML algorithms diagnose headaches as accurately as clinicians? A systematic review.'
Synthesis in Journal of neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
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Abstract
backgroundHeadache disorders are frequently misdiagnosed. We aimed to systematically evaluate the diagnostic accuracy, methodological quality, and clinical applicability of artificial intelligence (AI) and machine learning (ML) models for classifying adult headache disorders against clinician diagnoses using the International Classification of Headache Disorders (ICHD) criteria.
methodsIn this systematic review, we searched PubMed, Embase, and the Cochrane Library (January 2015-December 2025) for AI/ML diagnostic headache studies. Two reviewers extracted data and assessed risk of bias using the QUADAS-2 tool with the QUADAS-AI extension. Main outcomes were sensitivity, specificity, area under the receiver operating characteristic curve (AUC-ROC), and risk of bias.
resultsWe included 74 studies encompassing 154,856 participants. Models utilized traditional ML (n = 47), deep learning (n = 18), and hybrid or rule-based approaches (n = 9). Data inputs included neuroimaging (n = 27), multimodal datasets (n = 20), neurophysiological signals (n = 17), and clinical questionnaires (n = 10). Only 4 studies performed independent external validation. Overall sensitivity ranged from 47.5% to 100.0%, specificity from 50.4% to 100.0%, and AUC-ROC from 0.658 to 1.000. Models using structured questionnaires reported realistic accuracies (74-86%), whereas neuroimaging models frequently produced near-perfect, likely overfit estimates. Most studies (65 of 74) exhibited a high risk of bias driven by artificial case-control designs, data leakage, and absent external validation.
conclusionsAI-based headache diagnostic systems demonstrate promising but highly variable accuracy. Pervasive methodological flaws-specifically severe spectrum bias, data leakage, and a lack of independent external validation-currently preclude clinical implementation. Future studies require prospective, real-world validation to safely integrate these tools into practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.