Summary of Ensuring Safety and Trust: Analyzing the Risks Of Large Language Models in Medicine, by Yifan Yang et al.
Ensuring Safety and Trust: Analyzing the Risks of Large Language Models in Medicine
by Yifan Yang, Qiao Jin, Robert Leaman, Xiaoyu Liu, Guangzhi Xiong, Maame Sarfo-Gyamfi, Changlin Gong, Santiago Ferrière-Steinert, W. John Wilbur, Xiaojun Li, Jiaxin Yuan, Bang An, Kelvin S. Castro, Francisco Erramuspe Álvarez, Matías Stockle, Aidong Zhang, Furong Huang, Zhiyong Lu
First submitted to arxiv on: 20 Nov 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 The proposed framework for safe and trustworthy medical AI, consisting of five principles (Truthfulness, Resilience, Fairness, Robustness, and Privacy) and ten specific aspects, aims to address the risks associated with using Large Language Models (LLMs) in healthcare applications. The MedGuard benchmark is introduced, comprising 1,000 expert-verified questions, which evaluates the performance of 11 commonly used LLMs. The results show that these models generally perform poorly on most benchmarks, highlighting a significant safety gap and emphasizing the need for human oversight and AI safety guardrails. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are becoming increasingly popular in healthcare applications. However, there’s been little effort to understand the risks involved. A new framework is proposed to make sure these models are used safely and fairly. The framework includes five main principles: being truthful, resilient, fair, robust, and private. It also includes ten specific things to consider. To test this framework, a benchmark called MedGuard was created with 1,000 questions verified by experts. Eleven popular LLMs were tested against this benchmark, and they all performed poorly compared to human doctors. |
