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Machine Learning in E-Health and Digital Healthcare: Practical Strategies for Transformation
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Author(s): T. K. Sethuramalingam (Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, India), Rajkumar G. Nadakinamani (Badr Al Samaa Hospital, Oman), G. Sumathy (Department of Computational Intelligence, SRM Institute of Science and Technology, India)and Sureshkumar Myilsamy (Mechanical Engineering, Bannari Amman Institute of Technology, India)
Copyright: 2024
Pages: 29
Source title:
Handbook of Research on AI and ML for Intelligent Machines and Systems
Source Author(s)/Editor(s): Brij B. Gupta (Asia University, Taichung, Taiwan & Lebanese American University, Beirut, Lebanon)and Francesco Colace (University of Salerno, Italy)
DOI: 10.4018/978-1-6684-9999-3.ch012
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Abstract
Machine learning is revolutionizing healthcare by offering innovative solutions to complex challenges. This chapter explores the practical strategies, ethical considerations, and real-world applications of machine learning in the healthcare domain. It delves into data collection and management, model development, integration with existing systems, and the importance of interdisciplinary collaboration. The chapter also discusses the ethical dimensions of healthcare AI, such as data privacy, bias mitigation, and regulatory compliance. Real-world case studies highlight the impact of machine learning on early disease detection, drug discovery, and precision medicine. The chapter concludes by examining future trends, including emerging technologies like quantum computing, nanomedicine, and the growing role of AI in drug discovery and genomic medicine. As machine learning continues to reshape healthcare, understanding these practical strategies and ethical considerations is essential for optimizing patient care and advancing the healthcare industry.
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