Summary of Msdiagnosis: a Benchmark For Evaluating Large Language Models in Multi-step Clinical Diagnosis, by Ruihui Hou et al.
MSDiagnosis: A Benchmark for Evaluating Large Language Models in Multi-Step Clinical Diagnosis
by Ruihui Hou, Shencheng Chen, Yongqi Fan, Guangya Yu, Lifeng Zhu, Jing Sun, Jingping Liu, Tong Ruan
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel framework that enables large language models to self-evaluate and adjust their diagnostic results in a multi-step clinical diagnosis process. The framework combines forward inference, backward inference, reflection, and refinement to improve the accuracy of primary diagnosis, differential diagnosis, and final diagnosis. This is achieved through a Chinese clinical diagnostic benchmark called MSDiagnosis, which consists of 2,225 cases from 12 departments. The proposed method outperforms open-source models and closed-source models in experimental results, demonstrating its effectiveness. The paper also provides a comprehensive analysis and suggests future research directions for this task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to help doctors make better diagnoses by using artificial intelligence. Right now, most diagnostic tools are single-step processes that don’t match how doctors really work. This new system combines different steps to make more accurate diagnoses. It also includes a big dataset of real-world cases from 12 departments to test the model’s performance. The results show that this method is better than other models at making correct diagnoses. |
Keywords
» Artificial intelligence » Inference
