Sharing Data With Shared Benefits: Artificial Intelligence Perspective

被引:0
|
作者
Tajabadi, Mohammad [1 ]
Grabenhenrich, Linus [2 ]
Ribeiro, Adele [1 ]
Leyer, Michael [1 ,3 ]
Heider, Dominik [1 ]
机构
[1] Univ Marburg, Fac Math & Comp Sci, Dept Data Sci Biomed, Hans Meerwein Str 6, D-35043 Marburg, Germany
[2] Robert Koch Inst, Dept Methods Dev Res Infrastruct & Informat Techno, Berlin, Germany
[3] Queensland Univ Technol, Fac Business & Law, Sch Management, Brisbane, Qld, Australia
关键词
federated learning; machine learning; medical data; fairness; data sharing; artificial intelligence; development; artificial intelligence model; applications; data analysis; diagnostic tool; tool;
D O I
暂无
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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. (J Med Internet Res 2023;25:e47540) doi: 10.2196/47540
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页数:6
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