Summary of Leveraging Professional Radiologists’ Expertise to Enhance Llms’ Evaluation For Radiology Reports, by Qingqing Zhu et al.
Leveraging Professional Radiologists’ Expertise to Enhance LLMs’ Evaluation for Radiology Reports
by Qingqing Zhu, Xiuying Chen, Qiao Jin, Benjamin Hou, Tejas Sudharshan Mathai, Pritam Mukherjee, Xin Gao, Ronald M Summers, Zhiyong Lu
First submitted to arxiv on: 29 Jan 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 A novel approach synergizes large language models (LLMs) with professional radiologists to evaluate AI-generated radiology reports, addressing current limitations such as semantic intricacies and clinical detail emphasis. The proposed method utilizes In-Context Instruction Learning (ICIL), Chain of Thought (CoT) reasoning, and a regression model to align LLM evaluations with radiologist standards. Experimental results show that the “Detailed GPT-4” and “Regressed GPT-4” models outperform existing metrics in evaluating AI-generated reports. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI helps doctors write better medical reports! Researchers created a new way to evaluate these reports by combining artificial intelligence (AI) with the expertise of real doctors. They used big language models like GPT-3.5 and GPT-4 to help make decisions about what’s important in a report. This approach worked well and showed that AI can be really good at helping doctors write clear and useful medical reports. |
Keywords
» Artificial intelligence » Gpt » Regression
