Foundation models in healthcare require rethinking reliability

IF 18.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Nature Machine Intelligence Pub Date : 2024-11-11 DOI:10.1038/s42256-024-00924-5

Thomas Grote, Timo Freiesleben, Philipp Berens

引用次数: 0

Abstract

A new class of AI models, called foundation models, has entered healthcare. Foundation models violate several basic principles of the standard machine learning paradigm for assessing reliability, making it necessary to rethink what guarantees are required to establish warranted trust in them.

查看原文本刊更多论文

医疗保健领域的基础模式需要重新思考可靠性问题

一种被称为基础模型的新型人工智能模型已进入医疗保健领域。基础模型违反了用于评估可靠性的标准机器学习范式的几项基本原则，因此有必要重新思考需要哪些保证才能建立对基础模型的充分信任。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Nature Machine Intelligence Multiple-

CiteScore

36.90

自引率

2.10%

发文量

127

期刊介绍： Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.