Columbia University, Department of Biomedical Informatics, New York, 10032, USA.
National Institutes of Health, National Library of Medicine, National Center for Biotechnology Information, Bethesda, 20894, USA.
J Biomed Inform. 2024 May;153:104640. doi: 10.1016/j.jbi.2024.104640. Epub 2024 Apr 10.
Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence.
循证医学有望通过将最佳现有证据应用于医疗决策和实践来提高医疗质量。可以从各种来源获取的医疗证据呈快速增长趋势，这在收集、评估和综合证据信息方面带来了挑战。生成式人工智能的最新进展，例如大型语言模型，有望为这一艰巨任务提供便利。然而，开发负责任、公平和包容的模型仍然是一项复杂的任务。在此背景下，我们讨论了生成式人工智能在医疗证据自动摘要方面的可信度。
