SemioLLM: Evaluating Large Language Models for Diagnostic Reasoning from Unstructured Clinical Narratives in Epilepsy

Abstract: LLMs have been shown to encode clinical knowledge. Many evaluations, however, rely on structured question-answer benchmarks, overlooking critical challenges of interpreting and reasoning about unstructured clinical narratives in real-world settings. Using free-text clinical descriptions, we present SemioLLM, an evaluation framework that benchmarks 6 state-of-the-art models (GPT-3.5, GPT-4, Mixtral-8x7B, Qwen-72B, LlaMa2, LlaMa3) on a core diagnostic task in epilepsy. Leveraging a database of 1,269 seizure descriptions, we show that most LLMs are able to accurately and confidently generate probabilistic predictions of seizure onset zones in the brain. Most models approach clinician-level performance after prompt engineering, with expert-guided chain-of-thought reasoning leading to the most consistent improvements. Performance was further strongly modulated by clinical in-context impersonation, narrative length and language context (13.7%, 32.7% and 14.2% performance variation, respectively). However, expert analysis of reasoning outputs revealed that correct prediction can be based on hallucinated knowledge and deficient source citation accuracy, underscoring the need to improve interpretability of LLMs in clinical use. Overall, SemioLLM provides a scalable, domain-adaptable framework for evaluating LLMs in clinical disciplines where unstructured verbal descriptions encode diagnostic information. By identifying both the strengths and limitations of state-of-the-art models, our work supports the development of clinically robust and globally applicable AI systems for healthcare.