The Next Decade of Healthcare

Abstract

The progress of artificial intelligence (AI) in healthcare is significantly hindered by data privacy concerns, regulatory constraints, and fragmented data silos, which challenge traditional centralized training models. This chapter presents Federated Learning (FL) as a cornerstone technology for the next era of medical AI. FL is a decentralized paradigm that allows multiple institutions to collaboratively train robust models without sharing raw patient data, thereby preserving privacy by design. The chapter examines FL's core principles and architectures, contrasting them with the limitations of centralized AI. It showcases real-world applications in diagnostics, drug discovery, and genomics, while also addressing the technical and operational hurdles, such as data heterogeneity and security. Looking forward, we explore the future trajectory of FL, including its synergy with other privacy-enhancing technologies, the rise of personalized models (pFL).

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