A Comprehensive Review of Artificial Intelligence and Machine Learning Methods for Modern Healthcare Systems

Artificial Intelligence (AI) and Machine Learning (ML) methods have been applied significantly in modern healthcare systems in the last few years. AI and its subfields, such as ML, Deep Learning (DL), and Reinforcement Learning (RL), are driving a paradigm shift in modern healthcare systems, including disease detection, diagnosis, treatment, and outcome prediction, supported by good quality healthcare datasets. Research domains such as telemedicine, precision medicine, and healthcare monitoring have become pioneers in deploying AI methods for advancing medical sectors. Additionally, the emerging subfield of AI, Federated Learning (FL) removes the barrier of data sharing and enhances privacy which is gaining increasing attention as a mainstream technology in healthcare research and utilizing patients’ data efficiently. This paper provides a comprehensive study of the use of AI, ML, and FL methods in smart healthcare systems, their contributions to this paradigm shift, their current status, and their recent challenges. In addition, this study outlines a road map for future research in this domain.