The Digital Press

Collaborative AI research in medical imaging requires self-contained algorithms that can be easily shared, executed, and validated across sites, but human expertise complicates this process. This paper identifies detailed requirements and reviews existing APIs, highlighting gaps in interoperability and reusability for medical imaging AI.

Why it matters: Easier sharing and execution of AI algorithms can accelerate medical imaging research and improve clinical outcomes.

The big picture: The goal supports sustainable AI ecosystems aligned with FAIR principles, emphasizing interoperability and reusability.

The stakes: Without standardized solutions, expert supervision remains a bottleneck, slowing collaborative AI development and deployment.

Quick takeaway: No current API fully addresses all needed aspects for seamless, federated medical imaging AI algorithm use.