Foundations of Federated Intelligent Systems in Healthcare

Abstract

A new era in the healthcare industry has begun with the emergence of Artificial Intelligence (AI). AI plays a vital role by providing improved accuracy in diagnosis, operational efficiency, and personalized treatment. Traditionally, to perform predictions and calculations on patient data, the system works in a centralized manner. The system may face challenges and issues associated with the privacy and security of data with the constraints imposed by regularity bodies. Federated AI provides an innovative way of involving several users in a decentralized manner. This chapter discusses the concepts, including its training process, principles, and a comparison with conventional systems. It also highlights the utilization of Federated Learning (FL) in healthcare, the working of decentralized AI, the need and role of FL, data privacy, and security concerns. Practical aspects of FL in healthcare have been discussed through different case studies, such as a pilot study performed by Apple & Stanford medicine, brain imagining for Alzheimer's diagnosis, and mood detection have been discussed. The case studies highlight the impact of these frameworks on issues like privacy and security along with patient outcomes. The chapter also focuses on challenges and ethical considerations associated with Federated AI, including data diversity, traceability and accountability, transparency, trust, data ownership, and the need for federated governance frameworks. The ethical consideration also involves compliances imposed by the global regulations for utilizing the health industry data. Federated AI provides a framework for the health industry to work on heterogeneous datasets while maintaining the privacy and security of the data. The present chapter also provides an overview of the present state of the art for Federated AI with prospects. The chapter aims to guide policymakers, practitioners, and researchers in understanding the role of Federated AI for improved health outcomes and medical advancements.

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