.png){kind=icon}
返回
Sharing Data With Shared Benefits: Artificial Intelligence Perspective
.png){kind=icon}
OA
AI
DOI：10.2196/47540.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
federated learning
machine learning
medical data
fairness
data sharing
artificial intelligence
development
artificial intelligence model
applications
data analysis
diagnostic tool
tool
期刊
IF:
6
论文数:
9.7K
被引数:
5.0W
机构
引用论文
Ionic liquids derived from trialkylsulfonium bromides: Physicochemical properties and potential applications溴化三烷基锍衍生的离子液体: 物理化学性质和潜在应用
PREAI
分享收藏Swarm learning for decentralized artificial intelligence in cancer histopathology癌症组织病理学中分散人工智能的群学习.png)
NATURE MEDICINE
OAAI
NATURE MEDICINEIF50
OAAI分享收藏分享收藏
查看更多内容