John J. Hopfield and Geoffrey E. Hinton were awarded the 2024 Nobel Prize in Physics for developing machine learning technology using artificial neural networks. In Chemistry it was awarded to Demis Hassabis and John M. Jumper for developing an AI algorithm that solved the 50-year protein structure prediction challenge.

约翰·霍普菲尔德（JohnJ.Hopfield）和杰弗里·辛顿（GeoffreyE.Hinton）因利用人工神经网络开发机器学习技术而获得2024年诺贝尔物理学奖。在化学方面，它被授予Demis Hassabis和John M.Jumper，因为他们开发了一种AI算法，解决了50年来蛋白质结构预测的挑战。

This highlights AI’s impact on science, medicine and society; however, the winners acknowledge ethical aspects of AI that must be considered..

这突出了AI对科学，医学和社会的影响；然而，获奖者承认必须考虑人工智能的道德方面。。

In October 2024, the Nobel Committees in Stockholm announced that the prizes in Physics and Chemistry were awarded to work related to artificial intelligence (AI)1,2. The prize in Physics was awarded to John J. Hopfield and Geoffrey E. Hinton (formerly of Google) for “foundational discoveries and inventions that enable machine learning with artificial neural networks1.” The prize in Chemistry was awarded one-half to David Baker for “computational protein design” and one-half to Demis Hassabis and John M.

。物理学奖授予约翰·J·霍普菲尔德（JohnJ.Hopfield）和杰弗里·E·辛顿（GeoffreyE.Hinton）（前谷歌公司），表彰“利用人工神经网络进行机器学习的基础性发现和发明”。化学奖一半授予大卫·贝克（DavidBaker）“计算蛋白质设计”，一半授予黛米斯·哈萨比斯（DemisHassabis）和约翰·M。

Jumper (of DeepMind) for “protein structure prediction2.” The historic announcement of these Nobel Prizes for AI-related work has been widely discussed in mainstream media, with articles including “A Shift in the World of Science” by New York Times3 and “AI wins big at the Nobels” by The Economist4.

“蛋白质结构预测2”的Jumper（来自DeepMind）。这些与人工智能相关的诺贝尔奖的历史性宣布已在主流媒体上得到广泛讨论，其中包括《纽约时报》3的“科学世界的转变”和《经济学人》4的“人工智能在诺贝尔奖上大获全胜”。

This article summarizes the AI-related work of these Nobel laureates and discusses implications of their discoveries for medical science, the practice of medicine, and society (Fig. 1).Fig. 1: Summary of artificial intelligence work related to the 2024 Nobel Prizes in Physics and Chemistry.This is shown in timeline format.Full size image2024 Nobel Prize in PhysicsHopfield and Hinton developed methods that form the foundation of today’s machine learning (ML) technology1.

本文总结了这些诺贝尔奖获得者与人工智能相关的工作，并讨论了他们的发现对医学，医学实践和社会的影响（图1）。图1:2024年诺贝尔物理学和化学奖相关人工智能工作总结。这以时间线格式显示。全尺寸图像2024诺贝尔物理学奖霍普菲尔德和辛顿开发的方法构成了当今机器学习（ML）技术的基础1。

Hopfield invented the Hopfield network, an associative memory structure that can store and reconstruct information5. Building on the Hopfield network, Hinton developed the Boltzmann machine, a method that can autonomously discover properties in data6. These discoveries are fundamental to artificial neural networks, allowing them to sort and analyze vast amounts of data7.

霍普菲尔德发明了霍普菲尔德网络，这是一种可以存储和重建信息的联想记忆结构5。辛顿在霍普菲尔德网络的基础上开发了玻尔兹曼机器，这种方法可以自主发现数据中的属性6。这些发现是人工神经网络的基础，使它们能够对大量数据进行分类和分析7。

In turn, this allows computers to rapidly process information, learn effectively, and generate memory7. Today, neural networks provide compu.

反过来，这使计算机能够快速处理信息，有效学习并生成记忆7。今天，神经网络提供了计算机。

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Download referencesAuthor informationAuthors and AffiliationsDivision of Vascular Surgery, University of Toronto, Toronto, ON, CanadaBen LiTemerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, CanadaBen LiElse Kröner Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, GermanyStephen GilbertAuthorsBen LiView author publicationsYou can also search for this author in.

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PubMed Google ScholarContributionsB.L. and S.G. developed the concept of the manuscript. B.L. wrote the first draft of the manuscript. B.L. and S.G. contributed to the writing, interpretation of the content, and editing of the manuscript, revising it critically for important intellectual content.

PubMed谷歌学术贡献b。五十、 S.G.开发了手稿的概念。B、 L.写了手稿的初稿。B、 L.和S.G.为稿件的撰写，内容的解释和编辑做出了贡献，并对重要的知识内容进行了批判性的修改。

All authors had final approval of the completed version and take accountability for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Corresponding authorCorrespondence to.

所有作者都最终批准了完成的版本，并对工作的各个方面负责，以确保与工作任何部分的准确性或完整性有关的问题得到适当的调查和解决。对应作者对应。

Stephen Gilbert.Ethics declarations

史蒂芬·吉尔伯特。道德宣言

Competing interests

相互竞争的利益

B.L. declares no nonfinancial interests and no competing financial interests. S.G. declares anonfinancial interest as an Advisory Group member of the EY-coordinated “Study on RegulatoryGovernance and Innovation in the field of Medical Devices” conducted on behalf of the DG SANTE of the European Commission.

B、 L.声明没有非财务利益，也没有相互竞争的财务利益。S、 G.宣布anonfinancial interest为代表欧盟委员会DG SANTE进行的安永协调的“医疗器械领域监管和创新研究”咨询小组成员。

S.G. is the coordinator of a Bundesministerium für Bildung und Forschung (BMBF) project (Personal Mastery of Health & WellnessData, PATH) on consent in health data sharing, financed through the European Union NextGenerationEU program. S.G. declares the following competing financial interests: he has or has had consulting relationships with Una Health GmbH, Lindus Health Ltd., Flo Ltd, Thymia Ltd., FORUM Institut für Management GmbH, High-Tech Gründerfonds Management GmbH, Prova Health Ltd., and Ada Health GmbH and holds share options in Ada Health GmbH.

S、 G.是Bundesministerium für Bildung und Forschung（BMBF）项目（个人掌握健康与健康数据，PATH）的协调员，该项目通过欧盟下一代计划（European Union NextGenerationEU program）资助，同意健康数据共享。S、 G.宣布以下相互竞争的财务利益：他与Una Health GmbH，Lindus Health Ltd.，Flo Ltd，Thymia Ltd.，FORUM Institut für Management GmbH，High Tech Gründerfonds Management GmbH，Prova Health Ltd。和Ada Health GmbH有或曾经有过咨询关系，并持有Ada Health GmbH的股票期权。

S.G. is a News and Views Editor for npj Digital Medicine. S.G. played no role in the internal review or decision to publish this News and Views article..

S、 G.是npj数字医学的新闻和观点编辑。S、 G.在内部审查或决定发布此新闻和观点文章时未发挥任何作用。。

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Reprints and permissionsAbout this articleCite this articleLi, B., Gilbert, S. Artificial Intelligence awarded two Nobel Prizes for innovations that will shape the future of medicine.

转载和许可本文引用本文Li，B.，Gilbert，S。人工智能因将塑造医学未来的创新而获得两项诺贝尔奖。

npj Digit. Med. 7, 336 (2024). https://doi.org/10.1038/s41746-024-01345-9Download citationReceived: 27 October 2024Accepted: 15 November 2024Published: 25 November 2024DOI: https://doi.org/10.1038/s41746-024-01345-9Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

npj数字。医学杂志7336（2024）。https://doi.org/10.1038/s41746-024-01345-9Download引文接收日期：2024年10月27日接受日期：2024年11月15日发布日期：2024年11月25日OI：https://doi.org/10.1038/s41746-024-01345-9Share本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

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