利用生成式人工智能进行临床证据合成需要确保可信度

IF 4 2区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Biomedical Informatics Pub Date : 2024-04-10 DOI:10.1016/j.jbi.2024.104640

Gongbo Zhang , Qiao Jin , Denis Jered McInerney , Yong Chen , Fei Wang , Curtis L. Cole , Qian Yang , Yanshan Wang , Bradley A Malin , Mor Peleg , Byron C. Wallace , Zhiyong Lu , Chunhua Weng , Yifan Peng

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引用次数: 0

摘要

循证医学通过利用现有的最佳证据做出医疗决策和实践，有望提高医疗质量。医学证据的快速增长可以从各种来源获得，这给收集、评估和综合证据信息带来了挑战。以大型语言模型为代表的生成式人工智能的最新进展有望推动这项艰巨的任务。然而，开发负责任、公平和包容的模型仍然是一项复杂的工作。在本视角中，我们将结合医学证据的自动总结，讨论生成式人工智能的可信度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness

查看原文本刊更多论文

Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness

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.

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来源期刊

Journal of Biomedical Informatics 医学-计算机：跨学科应用

CiteScore

8.90

自引率

6.70%

发文量

243

审稿时长

32 days

期刊介绍： The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.