文献类型：期刊文献

英文题名：MSDIAGNOSIS: A Benchmark for Evaluating Large Language Models in Multi-Step Clinical Diagnosis

作者：Hou, Ruihui[1]; Chen, Shencheng[1]; Fan, Yongqi[1]; Yu, Guangya[1]; Zhu, Lifeng[2]; Sun, Jing[2]; Liu, Jingping[1]; Ruan, Tong[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China; [2] Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China

年份：2024

外文期刊名：arXiv

收录：EI(收录号：20240356265)

语种：英文

外文关键词：Benchmarking

摘要：Clinical diagnosis is critical in medical practice, typically requiring a continuous and evolving process that includes primary diagnosis, differential diagnosis, and final diagnosis. However, most existing clinical diagnostic tasks are single-step processes, which does not align with the complex multi-step diagnostic procedures found in real-world clinical settings. In this paper, we propose a Chinese clinical diagnostic benchmark, called MSDiagnosis. This benchmark consists of 2,225 cases from 12 departments, covering tasks such as primary diagnosis, differential diagnosis, and final diagnosis. Additionally, we propose a novel and effective framework. This framework combines forward inference, backward inference, reflection, and refinement, enabling the large language model to self-evaluate and adjust its diagnostic results. To this end, we test open-source models, closed-source models, and our proposed framework. The experimental results demonstrate the effectiveness of the proposed method. We also provide a comprehensive experimental analysis and suggest future research directions for this task. Copyright ? 2024, The Authors. All rights reserved.