Automatic differential diagnosis (DDx) is an essential medical task that generates a list of potential diseases as differentials based on patient symptom descriptions. In practice, interpreting these differential diagnoses yields significant value but remains under-explored. Given the powerful capabilities of large language models (LLMs), we investigated using LLMs for interpretable DDx. Specifically, we curated the first DDx dataset with expert-derived interpretation on 570 clinical notes. Besides, we proposed Dual-Inf, a novel framework that enabled LLMs to conduct bidirectional inference (i.e., from symptoms to diagnoses and vice versa) for DDx interpretation. Both human and automated evaluation validated its efficacy in predicting and elucidating differentials across four base LLMs. In addition, Dual-Inf could reduce interpretation errors and hold promise for rare disease explanations. To the best of our knowledge, it is the first work that customizes LLMs for DDx explanation and comprehensively evaluates their interpretation performance. Overall, our study bridges a critical gap in DDx interpretation and enhances clinical decision-making.

翻译：自动鉴别诊断（DDx）是一项关键的医疗任务，它基于患者症状描述生成一份潜在疾病作为鉴别诊断列表。在实践中，对这些鉴别诊断进行解释具有重要价值，但相关研究仍显不足。鉴于大语言模型（LLMs）的强大能力，我们研究了利用LLMs进行可解释的DDx。具体而言，我们构建了首个包含专家对570份临床记录进行解释的DDx数据集。此外，我们提出了Dual-Inf，一个新颖的框架，使LLMs能够进行双向推理（即从症状到诊断以及从诊断到症状）以实现DDx解释。人工和自动化评估均验证了其在四种基础LLMs上预测和阐明鉴别诊断的有效性。此外，Dual-Inf能够减少解释错误，并有望用于罕见病的解释。据我们所知，这是首个针对DDx解释定制LLMs并全面评估其解释性能的工作。总体而言，我们的研究弥合了DDx解释中的一个关键空白，并增强了临床决策能力。