Guidelines For Rigorous Evaluation of Clinical LLMs For Conversational Reasoning

Abstract

Abstract The integration of Large Language Models (LLMs) like GPT-4 and GPT-3.5 into clinical diagnostics has the potential to transform patient-doctor interactions. However, the readiness of these models for real-world clinical application remains inadequately tested. This paper introduces the Conversational Reasoning Assessment Framework for Testing in Medicine (CRAFT-MD), a novel approach for evaluating clinical LLMs. Unlike traditional methods that rely on structured medical exams, CRAFT-MD focuses on natural dialogues, using simulated AI agents to interact with LLMs in a controlled, ethical environment. We applied CRAFT-MD to assess the diagnostic capabilities of GPT-4 and GPT-3.5 in the context of skin diseases. Our experiments revealed critical insights into the limitations of current LLMs in terms of clinical conversational reasoning, history taking, and diagnostic accuracy. Based on these findings, we propose a comprehensive set of guidelines for future evaluations of clinical LLMs. These guidelines emphasize realistic doctor-patient conversations, comprehensive history taking, open-ended questioning, and a combination of automated and expert evaluations. The introduction of CRAFT-MD marks a significant advancement in LLM testing, aiming to ensure that these models augment medical practice effectively and ethically.

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