Summary of A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions, by Lei Liu et al.
A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions
by Lei Liu, Xiaoyan Yang, Junchi Lei, Yue Shen, Jian Wang, Peng Wei, Zhixuan Chu, Zhan Qin, Kui Ren
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 surveys the recent advancements in Medical Large Language Models (Med-LLMs), which have revolutionized medical artificial intelligence. The authors trace the evolution from general-purpose models to medical-specialized applications, highlighting key findings and mainstream techniques. They explore how Med-LLMs can assist clinicians, educators, and patients across various healthcare domains. The paper also discusses the challenges of ensuring fairness, accountability, privacy, and robustness in these models, emphasizing the need for rigorous evaluation methodologies and regulatory frameworks. The authors conclude by outlining potential future trajectories for Med-LLMs, emphasizing the importance of prudent expansion. This paper is relevant to professionals and researchers seeking a comprehensive understanding of Med-LLMs’ strengths and limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how Large Language Models are helping medicine. These models can help doctors, teachers, and patients in many ways. The authors talk about how they’re used in hospitals, schools, and homes. They also discuss some problems that need to be solved, like making sure the models are fair and safe. The authors think that these models will keep getting better and that we need to make sure they’re used wisely. |
