Summary of Large Language Models For Disease Diagnosis: a Scoping Review, by Shuang Zhou et al.
Large Language Models for Disease Diagnosis: A Scoping Review
by Shuang Zhou, Zidu Xu, Mian Zhang, Chunpu Xu, Yawen Guo, Zaifu Zhan, Sirui Ding, Jiashuo Wang, Kaishuai Xu, Yi Fang, Liqiao Xia, Jeremy Yeung, Daochen Zha, Genevieve B. Melton, Mingquan Lin, Rui Zhang
First submitted to arxiv on: 27 Aug 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 In a paradigm-shifting development, large language models (LLMs) have shown great promise in automatic disease diagnosis, a crucial task in clinical practice. This paper provides a holistic view of the current state of LLM-based methods for disease diagnosis by examining existing literature across various dimensions, including disease types, clinical data, LLM techniques, and evaluation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be used to diagnose diseases more accurately and efficiently than traditional methods. By reviewing existing research on LLM-based disease diagnosis, we can better understand the strengths and limitations of these models. This paper provides a comprehensive overview of the current state of LLM-based disease diagnosis and suggests ways to improve future research. |
