ArticleBMC neurology2025
Validation of an AI-Based platform for structured diagnosis of headache disorders using ICHD-3 criteria.
Article in BMC neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 'Man vs. Machine: can ML algorithms diagnose headaches as accurately as clinicians? A systematic review.'Journal of neurology · 2026Pooled it
- RapidNeuroGuide: an NLP-enhanced AI platform for digital headache triage and clinical decision support.BMC emergency medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe diagnosis of headache disorders remains a clinical challenge, particularly for non-specialists, due to the complexity of the International Classification of Headache Disorders, 3rd edition (ICHD-3), and the absence of biomarkers. Large language models (LLMs) represent a promising tool to support accurate and scalable diagnostic classification, especially in resource-limited settings.
objectiveTo validate the performance of a free, multilingual clinical decision support platform-Head.AI-designed to classify headache cases using GPT-4o and a structured implementation of ICHD-3.
methodsWe conducted an independent validation using 315 expert-generated vignettes representing 215 ICHD-3 diagnoses, input into Head.AI and three other platforms (Claude Sonnet 4.0, Grok 3.0, and Gemini 2.5). Outcomes included diagnostic accuracy (rank of correct diagnosis), calibration, and citation rate.
resultsThe algorithm correctly identified the top diagnosis in 89.5% of cases (vs. 74-80% in comparators), with a citation rate >97% and calibration (Brier score 0.153). It maintained consistent performance across primary and secondary headaches and achieved first-hypothesis accuracy >74% in difficult cases. Logistic regression confirmed Head.AI had significantly higher odds of correct classification (ORs vs. comparators: 2.04-2.86; all p < 0.01).
conclusionOur algorithm demonstrated high diagnostic accuracy across a broad spectrum of headache disorders, exceeding the performance reported in prior studies, though direct comparison should be interpreted with caution due to methodological differences. Its public availability, structured knowledge base, and educational potential make it a valuable contribution to AI-assisted headache care. The platform is freely accessible at www.head-ai.com.br .
Indexed as
Identifiers
What OpenQuestion holds
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.