MSDiagnosis: A Benchmark for Evaluating Large Language Models in Multi-Step Clinical Diagnosis
This addresses the need for better benchmarks in clinical AI to align with real-world multi-step diagnostic procedures, though it is incremental as it builds on existing diagnostic tasks.
The authors tackled the problem of evaluating large language models in multi-step clinical diagnosis by introducing MSDiagnosis, a Chinese benchmark with 2,225 cases across 12 departments, and proposed a framework combining forward/backward inference, reflection, and refinement, which demonstrated effectiveness in experiments.
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.