ArticleEpilepsia2025
Can artificial intelligence diagnose seizures based on patients' descriptions? A study of GPT-4.
Article in Epilepsia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Modern generative large language models in epilepsy care: a scoping review of current applications, challenges, and future directions.Acta epileptologica · 2026Review
- Large Language Models in Clinical Neurology: A Systematic Review.Research square · 2026Article
- A Survey on Medical Competence Evaluation Benchmarks for Large Language Models.Health care science · 2026Review
- AI language model applications for early diagnosis of childhood epilepsy based on unstructured first-visit patient narratives: A cohort study.Epileptic disorders : international epilepsy journal with videotape · 2025Article
- Using Open-Source Large Language Models to Identify Access to Germline Genetic Testing in Veterans With Breast Cancer From Unstructured Text.JCO clinical cancer informatics · 2025Article
- Can artificial intelligence diagnose seizures based on patients' descriptions? A study of GPT-4.Epilepsia · 2025Article
- Large language models for neurology: a mini review.Frontiers in digital health · 2025Review
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Abstract
objectiveGeneralist large language models (LLMs) have shown diagnostic potential in various medical contexts but have not been explored extensively in relation to epilepsy. This paper aims to test the performance of an LLM (OpenAI's GPT-4) on the differential diagnosis of epileptic and functional/dissociative seizures (FDS) based on patients' descriptions.
methodsGPT-4 was asked to diagnose 41 cases of epilepsy (n = 16) or FDS (n = 25) based on transcripts of patients describing their symptoms (median word count = 399). It was first asked to perform this task without additional training examples (zero-shot) before being asked to perform it having been given one, two, and three examples of each condition (one-, two, and three-shot). As a benchmark, three experienced neurologists performed this task without access to any additional clinical or demographic information (e.g., age, gender, socioeconomic status).
resultsIn the zero-shot condition, GPT-4's average balanced accuracy was 57% (κ = .15). Balanced accuracy improved in the one-shot condition (64%, κ = .27), but did not improve any further in the two-shot (62%, κ = .24) and three-shot (62%, κ = .23) conditions. Performance in all four conditions was worse than the mean balanced accuracy of the experienced neurologists (71%, κ = .42). However, in the subset of 18 cases that all three neurologists had "diagnosed" correctly (median word count = 684), GPT-4's balanced accuracy was 81% (κ = .66). SIGNIFICANCE: Although its "raw" performance was poor, GPT-4 showed noticeable improvement having been given just one example of a patient describing epilepsy and FDS. Giving two and three examples did not further improve performance, but the finding that GPT-4 did much better in those cases correctly diagnosed by all three neurologists suggests that providing more extensive clinical data and more elaborate approaches (e.g., more refined prompt engineering, fine-tuning, or retrieval augmented generation) could unlock the full diagnostic potential of LLMs.
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