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.

翻译：临床决策支持系统（CDSS）利用循证医学知识和患者数据提供实时建议，而大型语言模型（LLM）作为一种生成医疗决策文本解释的有效工具正崭露头角。本研究探讨了LLM基于患者主诉生成诊断解释的有效性和可靠性。三位经验丰富的医生评估了LLM生成的、关于患者主诉与医生及模型指定诊断之间关联的多个阶段解释。实验结果表明，LLM的解释显著提高了医生对给定诊断的认同率，同时揭示了LLM输出中5%至30%的潜在错误。本研究强调了LLM在医疗领域的潜力与挑战，并指出为确保患者安全和最佳临床效用，需谨慎进行整合与评估。