Summary of Large Language Models For Medicine: a Survey, by Yanxin Zheng et al.
Large Language Models for Medicine: A Survey
by Yanxin Zheng, Wensheng Gan, Zefeng Chen, Zhenlian Qi, Qian Liang, Philip S. Yu
First submitted to arxiv on: 20 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 This paper reviews the recent advancements in large language models (LLMs), with a focus on their applications in medicine. Medical LLMs have the potential to revolutionize various aspects of healthcare, such as diagnosis and treatment planning. The authors provide an overview of existing medical LLMs, highlighting their strengths and weaknesses. They also discuss the challenges faced during their development, including data quality issues and the need for domain-specific knowledge. To overcome these hurdles, the authors suggest technical integration strategies and propose future research directions to improve the performance and applicability of medical LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to help doctors make better decisions. It talks about special kinds of computer programs called language models that can understand and process large amounts of text, like doctor’s notes or patient information. These models are important for medicine because they could help diagnose diseases more accurately or suggest the best treatments. The authors explain what these models do well and what they don’t do so well, and they talk about how to make them even better in the future. |
