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

arXiv: https://arxiv.org/abs/2408.10039 · HTML: https://mlsanity.com/paper/2408.10039/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2024.

## Built on by (most cited first, 2 of 2)

- [Evaluating the Pre-Consultation Ability of LLMs using Diagnostic Guidelines](https://mlsanity.com/paper/2601.03627/) (2026)
- [CMQCIC-Bench: A Chinese Benchmark for Evaluating Large Language Models in Medical Quality Control Indicator Calculation](https://mlsanity.com/paper/2502.11703/) (2025)

## Abstract (arXiv)

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.