Sharing Data With Shared Benefits: Artificial Intelligence Perspective.

Area of Science:

• Medical Artificial Intelligence
• Collaborative Data Analysis
• Health Informatics

Background:

• Developing powerful AI models for healthcare requires extensive data from multiple centers.
• Data privacy concerns and proprietary restrictions limit public data sharing.
• Existing federated and swarm learning methods lack incentives for private sector collaboration.

Purpose of the Study:

• To propose a novel solution for fair data contribution and AI model ownership in collaborative healthcare AI.
• To incentivize companies to participate in data sharing and AI model development.
• To ensure equitable benefits from collective data analysis for improved diagnostic tools.

Main Methods:

• Exploration of a viewpoint for a new collaborative data analysis and AI modeling approach.
• Focus on a system where participants receive AI models commensurate with their data contribution.
• Conceptual framework for fair incentive structures in private sector AI development.

Main Results:

• A proposed approach to address the limited incentive for companies in collaborative AI.
• Potential for a fairer distribution of AI model benefits based on data contribution.
• Facilitation of increased participation in collective data analysis for healthcare AI.

Conclusions:

• The proposed approach can encourage companies to engage in collaborative data analysis.
• Fairer data contribution models can lead to the development of improved diagnostic tools for all participants.
• This viewpoint offers a pathway to more equitable and effective healthcare AI development.