Bridging Gaps in Patient Care With AI-Driven IoMT

Abstract

This chapter explores the integration of artificial intelligence (AI) within the internet of medical things (IoMT) to address significant challenges in contemporary healthcare. The focus is on communication barriers, data fragmentation, and resource allocation issues, advocating for AI-driven solutions such as federated learning, privacy-preserving techniques, and multi-party communications. Real-world case studies illustrate the tangible impact of AI on improving diagnosis, treatment, and patient engagement. Ethical considerations, challenges, and lessons learned provide a comprehensive understanding of the implementation landscape. Practical recommendations for implementation, including strategic frameworks and regulatory considerations, guide stakeholders in navigating this transformative journey. In summary, the chapter serves as a valuable resource for healthcare professionals, policymakers, and researchers, offering insights into the evolving landscape of patient care through AI-driven IoMT, to optimize healthcare delivery and address critical gaps in the healthcare system.

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