Clinical Decision Support Systems (CDSS) utilize evidence-based knowledge and patient data to offer real-time recommendations, with Large Language Models (LLMs) emerging as a promising tool to generate plain-text explanations for medical decisions. This study explores the effectiveness and reliability of LLMs in generating explanations for diagnoses based on patient complaints. Three experienced doctors evaluated LLM-generated explanations of the connection between patient complaints and doctor and model-assigned diagnoses across several stages. Experimental results demonstrated that LLM explanations significantly increased doctors' agreement rates with given diagnoses and highlighted potential errors in LLM outputs, ranging from 5% to 30%. The study underscores the potential and challenges of LLMs in healthcare and emphasizes the need for careful integration and evaluation to ensure patient safety and optimal clinical utility.

本研究探讨了大型语言模型在生成患者病情诊断解释方面的可靠性和有效性，实验证明大型语言模型生成的解释显著提高了医生对给定诊断的一致性，同时揭示了大型语言模型输出的潜在错误，在医疗保健领域中的潜力和挑战得到了强调，强调了在整合和评估过程中需要谨慎以确保患者安全和优化的临床效用。

解读诊断：大型语言模型解释如何影响临床决策