Summary of A Survey For Large Language Models in Biomedicine, by Chong Wang et al.
A Survey for Large Language Models in Biomedicine
by Chong Wang, Mengyao Li, Junjun He, Zhongruo Wang, Erfan Darzi, Zan Chen, Jin Ye, Tianbin Li, Yanzhou Su, Jing Ke, Kaili Qu, Shuxin Li, Yi Yu, Pietro Liò, Tianyun Wang, Yu Guang Wang, Yiqing Shen
First submitted to arxiv on: 29 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 This review provides a comprehensive analysis of large language models (LLMs) in biomedicine, focusing on their practical implications in real-world biomedical contexts. The authors analyzed 484 publications from databases like PubMed, Web of Science, and arXiv to examine the current landscape, applications, challenges, and prospects of LLMs. They explored the capabilities of LLMs in zero-shot learning across various biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine. Additionally, they discussed adaptation strategies for fine-tuning uni-modal and multi-modal LLMs to enhance performance in specialized contexts. The review also highlighted challenges faced by LLMs in biomedicine, such as data privacy concerns, limited model interpretability, and ethics. To address these challenges, the authors identified future research directions, including federated learning methods for preserving data privacy and integrating explainable AI methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are super smart computers that can understand and generate human-like language. This paper looks at how LLMs are used in medicine to help diagnose diseases, find new medicines, and create personalized treatment plans. The researchers studied many papers on this topic and found that LLMs are really good at doing some tasks, but not others. They also talked about ways to make LLMs work better in medicine, like teaching them to focus on specific things or use multiple types of data. However, the authors also highlighted some problems with using LLMs in medicine, such as keeping people’s personal health information private and making sure the computers are fair and honest. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning » Multi modal » Zero shot
