Suppr超能文献

共享数据,共获收益:人工智能视角。

Sharing Data With Shared Benefits: Artificial Intelligence Perspective.

机构信息

Department of Data Science in Biomedicine, Faculty of Mathematics and Computer Science, University of Marburg, Marburg, Germany.

Department for Methods Development, Research Infrastructure and Information Technology, Robert Koch Institute, Berlin, Germany.

出版信息

J Med Internet Res. 2023 Aug 29;25:e47540. doi: 10.2196/47540.

Abstract

Artificial intelligence (AI) and data sharing go hand in hand. In order to develop powerful AI models for medical and health applications, data need to be collected and brought together over multiple centers. However, due to various reasons, including data privacy, not all data can be made publicly available or shared with other parties. Federated and swarm learning can help in these scenarios. However, in the private sector, such as between companies, the incentive is limited, as the resulting AI models would be available for all partners irrespective of their individual contribution, including the amount of data provided by each party. Here, we explore a potential solution to this challenge as a viewpoint, aiming to establish a fairer approach that encourages companies to engage in collaborative data analysis and AI modeling. Within the proposed approach, each individual participant could gain a model commensurate with their respective data contribution, ultimately leading to better diagnostic tools for all participants in a fair manner.

摘要

人工智能(AI)和数据共享是相辅相成的。为了开发用于医疗和健康应用的强大 AI 模型,需要在多个中心收集和汇集数据。然而,由于数据隐私等各种原因,并非所有数据都可以公开提供或与其他方共享。联邦学习和群智学习可以帮助解决这些场景下的问题。但是,在私营部门(例如公司之间),激励措施是有限的,因为由此产生的 AI 模型可供所有合作伙伴使用,而不论其各自的贡献如何,包括每个方提供的数据量。在这里,我们作为一个观点来探讨解决这一挑战的潜在方法,旨在建立一种更公平的方法,鼓励公司参与协作数据分析和 AI 建模。在提出的方法中,每个参与者都可以获得与其各自数据贡献相称的模型,最终以公平的方式为所有参与者提供更好的诊断工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a4a/10498316/6f8938819f0c/jmir_v25i1e47540_fig1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验