Suppr超能文献

注意力并非全部所需:在医疗保健和医学中使用大型语言模型所涉及的复杂伦理问题。

Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine.

机构信息

Digital Health Cooperative Research Centre, Melbourne, Australia.

出版信息

EBioMedicine. 2023 Apr;90:104512. doi: 10.1016/j.ebiom.2023.104512. Epub 2023 Mar 15.

Abstract

Large Language Models (LLMs) are a key component of generative artificial intelligence (AI) applications for creating new content including text, imagery, audio, code, and videos in response to textual instructions. Without human oversight, guidance and responsible design and operation, such generative AI applications will remain a party trick with substantial potential for creating and spreading misinformation or harmful and inaccurate content at unprecedented scale. However, if positioned and developed responsibly as companions to humans augmenting but not replacing their role in decision making, knowledge retrieval and other cognitive processes, they could evolve into highly efficient, trustworthy, assistive tools for information management. This perspective describes how such tools could transform data management workflows in healthcare and medicine, explains how the underlying technology works, provides an assessment of risks and limitations, and proposes an ethical, technical, and cultural framework for responsible design, development, and deployment. It seeks to incentivise users, developers, providers, and regulators of generative AI that utilises LLMs to collectively prepare for the transformational role this technology could play in evidence-based sectors.

摘要

大型语言模型(LLMs)是生成式人工智能(AI)应用的关键组成部分,可根据文本指令生成新的内容,包括文本、图像、音频、代码和视频。如果没有人类的监督、指导以及负责任的设计和运营,这种生成式 AI 应用将仍然只是一个噱头,具有在前所未有的规模上生成和传播错误信息或有害和不准确内容的巨大潜力。然而,如果将其负责任地定位和开发为人类的助手,增强而不是取代他们在决策、知识检索和其他认知过程中的作用,那么它们可以演变成高度高效、值得信赖、辅助信息管理的工具。本观点描述了这些工具如何改变医疗保健和医学领域的数据管理工作流程,解释了底层技术的工作原理,评估了风险和局限性,并为负责任的设计、开发和部署提出了一个伦理、技术和文化框架。它旨在激励生成式 AI 的用户、开发人员、提供者和监管者利用 LLM 共同为这项技术在循证领域可能发挥的变革性作用做好准备。

相似文献

引用本文的文献

5
Using large language models to extract information from pediatric clinical reports.使用大语言模型从儿科临床报告中提取信息。
PLOS Digit Health. 2025 Jul 23;4(7):e0000919. doi: 10.1371/journal.pdig.0000919. eCollection 2025 Jul.
10
Ethical Considerations for Generative Artificial Intelligence in Plastic Surgery.整形手术中生成式人工智能的伦理考量
Plast Reconstr Surg Glob Open. 2025 Jun 2;13(6):e6825. doi: 10.1097/GOX.0000000000006825. eCollection 2025 Jun.

本文引用的文献

2
Large language models encode clinical knowledge.大语言模型编码临床知识。
Nature. 2023 Aug;620(7972):172-180. doi: 10.1038/s41586-023-06291-2. Epub 2023 Jul 12.
5
Using cognitive psychology to understand GPT-3.利用认知心理学理解 GPT-3。
Proc Natl Acad Sci U S A. 2023 Feb 7;120(6):e2218523120. doi: 10.1073/pnas.2218523120. Epub 2023 Feb 2.
6
ChatGPT and Other Large Language Models Are Double-edged Swords.ChatGPT和其他大型语言模型是双刃剑。
Radiology. 2023 Apr;307(2):e230163. doi: 10.1148/radiol.230163. Epub 2023 Jan 26.
9
Multimodal biomedical AI.多模态生物医学人工智能。
Nat Med. 2022 Sep;28(9):1773-1784. doi: 10.1038/s41591-022-01981-2. Epub 2022 Sep 15.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验