Summary of Evaluating Large Language Models in Medical Applications: a Survey, by Xiaolan Chen et al.
Evaluating large language models in medical applications: a survey
by Xiaolan Chen, Jiayang Xiang, Shanfu Lu, Yexin Liu, Mingguang He, Danli Shi
First submitted to arxiv on: 13 May 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 paper presents a comprehensive overview of the landscape of evaluating large language models (LLMs) in medical contexts. The authors synthesize insights from existing studies and identify key challenges and opportunities in medical LLM evaluation, emphasizing the need for continued research to ensure responsible integration into clinical practice. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study evaluates the performance of LLMs in healthcare and medicine, highlighting their potential for tasks like clinical decision support and patient education. The authors provide an overview of existing studies on medical LLM evaluation, including data sources, task scenarios, and methods. They also identify challenges and opportunities for responsible integration into clinical practice. |
