Collaborative Health Intelligence

Abstract

Federated Learning (FL) is emerging as a transformative approach to AI-driven medical care, allowing for decentralized, privacy-preserving model training across multiple healthcare institutions. This chapter explores the potential of FL in enhancing healthcare systems, from personalized treatment recommendations to improving disease prediction accuracy. By enabling the secure sharing of healthcare data while ensuring patient privacy, FL has the capacity to address critical challenges such as data silos and biased models. However, challenges remain in areas like model convergence, communication efficiency, and data heterogeneity. Further the chapter discuss the ethical, regulatory, and collaborative dimensions necessary for the successful deployment of FL in healthcare. The future of AI-driven medical care hinges on advancements in FL techniques, integration with IoMT, and interdisciplinary collaboration for more efficient, transparent, and personalized healthcare.

Ähnliche Arbeiten