MedClarify：一种用于医学诊断的信息寻求AI代理，具有病例特定的后续问题
MedClarify: An information-seeking AI agent for medical diagnosis with case-specific follow-up questions
AI总结：
MedClarify通过生成病例特定的后续问题，提升医学诊断的准确性与可靠性。
摘要：
大型语言模型（LLMs）越来越多地用于医学中的诊断任务。在临床实践中，正确的诊断很少能立即从初始患者情况推断出来。相反，达到诊断通常涉及系统性的病史采集，期间医生通过迭代提问来推理多个潜在诊断以消除不确定性。这一过程需要考虑鉴别诊断并主动排除需要立即干预的紧急情况。然而，医学LLMs生成信息性后续问题并因此推理鉴别诊断的能力仍处于探索阶段。在此，我们介绍了MedClarify，一种用于信息寻求的AI代理，能够生成后续问题以支持诊断决策。具体而言，MedClarify生成类似于鉴别诊断的候选诊断列表，然后主动生成旨在减少诊断不确定性的后续问题。通过选择预期信息增益最高的问题，MedClarify实现了有针对性的、基于不确定性的推理以提高诊断性能。在我们的实验中，我们首先展示了当前LLMs在医学推理中的局限性，通常会生成多个同样可能的诊断，尤其是当患者病例不完整或诊断相关的信息缺失时。然后我们显示，我们的信息论推理方法可以生成有效的后续提问，从而将诊断错误率降低约27个百分点（p.p.）相比标准单次LLM基线。总之，MedClarify提供了一条通过代理信息寻求改进医学LLMs的道路，从而促进与能够反映真实世界临床推理迭代和不确定性质的医疗LLM的有效对话。
英文摘要：
Large language models (LLMs) are increasingly used for diagnostic tasks in medicine. In clinical practice, the correct diagnosis can rarely be immediately inferred from the initial patient presentation alone. Rather, reaching a diagnosis often involves systematic history taking, during which clinicians reason over multiple potential conditions through iterative questioning to resolve uncertainty. This process requires considering differential diagnoses and actively excluding emergencies that demand immediate intervention. Yet, the ability of medical LLMs to generate informative follow-up questions and thus reason over differential diagnoses remains underexplored. Here, we introduce MedClarify, an AI agent for information-seeking that can generate follow-up questions for iterative reasoning to support diagnostic decision-making. Specifically, MedClarify computes a list of candidate diagnoses analogous to a differential diagnosis, and then proactively generates follow-up questions aimed at reducing diagnostic uncertainty. By selecting the question with the highest expected information gain, MedClarify enables targeted, uncertainty-aware reasoning to improve diagnostic performance. In our experiments, we first demonstrate the limitations of current LLMs in medical reasoning, which often yield multiple, similarly likely diagnoses, especially when patient cases are incomplete or relevant information for diagnosis is missing. We then show that our information-theoretic reasoning approach can generate effective follow-up questioning and thereby reduces diagnostic errors by ~27 percentage points (p.p.) compared to a standard single-shot LLM baseline. Altogether, MedClarify offers a path to improve medical LLMs through agentic information-seeking and to thus promote effective dialogues with medical LLMs that reflect the iterative and uncertain nature of real-world clinical reasoning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
