Summary of Beyond Multiple-choice Accuracy: Real-world Challenges Of Implementing Large Language Models in Healthcare, by Yifan Yang et al.
Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcare
by Yifan Yang, Qiao Jin, Qingqing Zhu, Zhizheng Wang, Francisco Erramuspe Álvarez, Nicholas Wan, Benjamin Hou, Zhiyong Lu
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 LLMs have revolutionized the medical domain with human-level capabilities, leading to increased efforts to explore their applications in various healthcare settings. Despite this promise, several challenges remain for real-world uses of LLMs. This work highlights four key aspects that hinder the adoption of LLMs: operational vulnerabilities, ethical and social considerations, performance and assessment difficulties, and legal and regulatory compliance. To unlock the full potential of LLMs, it is essential to address these challenges and ensure their responsible integration into healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models have huge potential in medicine, but there are many problems that need to be solved. The biggest issues are about how we use them safely and fairly. This paper talks about four main challenges: making sure they work well in different situations, thinking about the ethics of using AI in healthcare, measuring how good they are at specific tasks, and following the rules and laws around AI use. |
