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Headache Diagnosis with Open Language Models on German Vignettes: Study Protocol.

Dorian Zwanzig¹, Anika Zahn², Sebastian Strauss²

¹Eberswalde University for Sustainable Development, Eberswalde, Germany.

Studies in Health Technology and Informatics

|May 23, 2026

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Large language models show promise in diagnosing headache disorders, achieving high accuracy in pilot studies. This research will further evaluate their potential as diagnostic support tools for complex headache conditions.

Area of Science:

Neurology
Artificial Intelligence
Medical Informatics

Background:

Headache disorders are diagnostically challenging due to clinical heterogeneity and complex classifications (ICHD-3).
Large language models (LLMs) show potential in diagnostic reasoning, but their application in headache medicine requires systematic evaluation.

Purpose of the Study:

To investigate the accuracy of LLMs in classifying headache disorders based on anonymized clinical vignettes.
To assess the potential of LLMs as diagnostic support tools in specialized headache care.

Main Methods:

A retrospective, multicentre study using anonymized clinical vignettes from headache centres.
Open-weight LLMs operating in a GDPR-compliant environment will classify cases.
LLM classifications will be compared against expert diagnoses (gold standard) using metrics like accuracy, precision, recall, F1 score, and Cohen's κ.

Keywords:

ICHD-3 diagnostic accuracy diagnostic support headache disorders large language models

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Main Results:

Preliminary pilot data (n=50) showed exact accuracy up to 62% and group-level accuracy up to 92%.
The study aims to confirm substantial agreement (Cohen's κ > 0.6) between LLM and expert diagnoses.

Conclusions:

LLMs demonstrate significant potential for accurate headache disorder classification.
This study will provide evidence for the utility of LLMs as valuable diagnostic support tools in headache medicine.