Model centric collaboration reduces data sharing barriers in medical artificial intelligence

Abstract

Medical artificial intelligence (AI) offers transformative potential for earlier disease detection, equitable access, and safer, more consistent care. However, its advancement depends on large-scale, multimodal, and longitudinal clinical data, which are highly sensitive and difficult to share under increasingly strict privacy and regulatory constraints. Federated learning-based on local training with gradient or parameter sharing-has emerged as a partial response to these challenges, yet it continues to face important limitations, including reliance on centralized aggregation, communication overhead, and reduced efficiency in heterogeneous settings. This perspective outlines a conceptual framework for rethinking collaboration in medical AI. We introduce the forward-looking notion of a data-model network, a model-centric paradigm that shifts collaboration from direct data exchange toward structured model interaction. In this framework, healthcare institutions train models locally and exchange model artifacts-such as parameters, distilled representations, or weak-to-strong generalizations-within a decentralized ecosystem, without transferring raw patient data. This theoretical model highlights how model-centric interaction can enable collective learning across heterogeneous clinical environments while preserving data sovereignty and embedding privacy protection by design. At the same time, we emphasize that this paradigm entails its own trade-offs and implementation challenges, including governance complexity, knowledge incompleteness, and trust management. By articulating these opportunities and limitations, this perspective argues that privacy-aware collaboration architectures can reposition privacy compliance from a constraint into a potential driver of innovation in medical AI-without presenting it as a panacea.

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