Summary of Enhancing Llms For Impression Generation in Radiology Reports Through a Multi-agent System, by Fang Zeng et al.
Enhancing LLMs for Impression Generation in Radiology Reports through a Multi-Agent System
by Fang Zeng, Zhiliang Lyu, Quanzheng Li, Xiang Li
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduced “RadCouncil” framework is a multi-agent Large Language Model (LLM) designed to improve the generation of impressions in radiology reports from the finding section. It consists of three agents: Retrieval, Radiologist, and Reviewer. The Retrieval agent retrieves similar reports, the Radiologist agent generates impressions based on these retrieved reports, and the Reviewer agent evaluates the generated impressions and provides feedback. RadCouncil’s performance was evaluated using BLEU, ROUGE, BERTScore, and qualitative criteria assessed by GPT-4, with chest X-ray as a case study. The results show improvements over the single-agent approach in diagnostic accuracy, stylistic concordance, and clarity. This study demonstrates the potential of multi-agent LLMs for enhancing performance in specialized medical tasks and developing more robust healthcare AI solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RadCouncil is a special computer program that helps doctors write better reports about X-ray pictures. It has three parts: one finds similar reports, another writes new reports based on those found ones, and the last part checks if the report makes sense. They tested it using real X-rays and it got better at writing reports than just one part working alone. This might help make medicine’s AI smarter. |
Keywords
» Artificial intelligence » Bleu » Gpt » Large language model » Rouge
