The Impact of Federated Learning on AI-Enhanced Healthcare Delivery

Abstract

Federated learning has recently emerged as a promising methodology in healthcare, seamlessly integrating with machine learning models to glean insights from distributed healthcare databases while prioritizing data privacy and security. This systematic literature review provides a comprehensive analysis of federated learning's current state in healthcare, highlighting trends in its fusion with AI and its advancements in analyzing medical reputation and predicting potential gains. The ethical and technical aspects of successful implementation and adoption to drive AI in healthcare are discussed. In conclusion, the integration of AI in healthcare is an evolutionary force, revolutionizing medical practices, treatment, patient care, and diagnosis, with federated learning playing a pivotal role in enabling collaborative, privacy-preserving, and data-driven healthcare transformations.

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