Transforming Healthcare with Federated Learning-based Artficial Intelligence: Concepts, Classifications, and Challenges

The realm of intelligent healthcare has seen a surge in discussions about transformative technologies, with a focus on federated learning (FL), artificial intelligence (AI), and explainable AI (XAI). Traditionally, healthcare data has been centralized, with a limited number of entities sharing raw data across the industry. However, this approach poses significant risks and challenges. Introducing AI into the healthcare ecosystem opens the door to a multitude of agent collaborators, each capable of working alongside their chosen host. The decentralized nature of FL is a noteworthy aspect, as it ensures that raw data remains within its source while enabling model-based communication within the system. The fusion of FL, AI, and XAI offers the potential to address some of the system's limitations and challenges. This article delves deep into the ways in which FL is revolutionizing healthcare facilities through AI integration. We begin by examining the current landscape of futuristic technologies, including FL, AI, XAI, and their relationship with the healthcare sector. We categorize FL in conjunction with AI, exploring a wide array of applications in healthcare IT. Key issues within the healthcare domain, such as privacy, security, stability, and reliability, are addressed. Furthermore, we showcase practical use cases of FL and AI in medical settings. We conclude by shedding light on the various future directions and avenues of research in the management of healthcare using FL-based AI. In this article, we offer insights into the development and implementation of the Contribution-Aware Federated Learning (CAreFL) system for intelligent healthcare. This system not only optimizes the FL model aggregation process based on evaluation results but also assesses the contributions of FL participants in a fair and transparent manner, ensuring efficiency and confidentiality.