Artificial Intelligence Techniques Based on Federated Learning in Smart Healthcare
Kanchan Naithani, Yadav Prasad Raiwani, Shrikant Tiwari, Alok Singh Chauhan · 2024
The use of artificial intelligence in intelligent healthcare systems has emerged as a paradigm shift that holds the promise of improved patient care, more effective utilization of resources, and increased diagnostic precision. In the domain of healthcare, federated learning has emerged as a technology that is both dependable and protects individuals’ privacy when it comes to decentralized data processing. Within the context of intelligent healthcare applications, this chapter analyzes the relationship between artificial intelligence methodologies and federated learning. In this chapter of the book, the most cutting-edge artificial intelligence algorithms that are used in federated learning for healthcare applications are presented. The landscape of this diverse field is constantly shifting, as seen by the insights revealed into current research trends and future intentions. A complete review of the integration of artificial intelligence techniques with federated learning in smart healthcare is provided in this chapter of the book. The chapter also highlights the potential benefits, limits, and ethical issues of this integration. The practical use of federated learning in a range of healthcare settings, including sickness prediction, tailored therapeutic recommendations, and remote patient monitoring, is shown via case studies and examples taken from the real-world. In addition, the chapter examines the ethical and legal issues of using artificial intelligence algorithms that are based on federated learning in the context of smart healthcare. Researchers, practitioners, and policymakers who are interested in using artificial intelligence to improve healthcare while maintaining data privacy and security will find this resource to be of great use.