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arXiv · 2408.10039

MSDiagnosis: A Benchmark for Evaluating Large Language Models in Multi-Step Clinical Diagnosis

Ruihui Hou·Shencheng Chen·Yongqi Fan·Guangya Yu·Lifeng Zhu·Jing Sun·Jingping Liu·Tong Ruan
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Abstract

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.

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Ruihui Hou, Shencheng Chen, Yongqi Fan, Guangya Yu, Lifeng Zhu, Jing Sun, Jingping Liu, Tong Ruan. 2024-12-16. MSDiagnosis: A Benchmark for Evaluating Large Language Models in Multi-Step Clinical Diagnosis. https://arxiv.org/abs/2408.10039

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