Security protocols in healthcare: A comprehensive study of AI-Enabled IoMT
1 Meta, Dallas, Texas, USA.
2 Microsoft, USA.
3 Department Computer Science & Engineering (IoT), Vasireddy Venkatadri Institute of Technology, Nambur, India.
Review Article
Magna Scientia Advanced Biology and Pharmacy, 2024, 12(01), 032–037
Article DOI: 10.30574/msabp.2024.12.1.0030
Publication history:
Received on 24 March 2024; revised on 05 May 2024; accepted on 08 May 2024
Abstract:
The Internet of Medical Things IoMT has driven a paradigm shift in the way things have been done traditionally within medical Healthcare practices and this is evidence that Smart Personal systems (Health Care) are shaping up with mighty forces. By merging these technologies, IoT has transformed data communication and provided the means to directly interconnect medical devices and sensor systems. The use of advanced technologies like Artificial Intelligence( AI), Machine Learning, Deep Learning, and Federated Learning has highly equipped and transformed the IoMT’s performance. These integrations also raise concerns on data privacy and security, and addressing these challenges become more important as without proper security and privacy measures people can face serious disruption both online and offline leading to upcoming catastrophes already waiting at its end line. This chapter looks into the intricacies of a highly secure communications system powered by AI, specifically in the age where disasters like COVID-19 [10], emphasizes the need for increased security on data transmission as well as storage. IoMT has wide applications for integrating remote patient monitoring, enhancing user satisfaction and interdimensional hospitalization rates, we delve into how IoMT works in conjunction with machine learning, particularly federated Learning intends to address any possible concerns. The chapter begins with IoMT systems, including IoMT ecosystems, implantable medical devices IMDs, and Internet-worn gadgetry. Special attention is given to the AI-powered Intrusion Detection Systems (IDSs) in IoMT environments, while network security remains the research focus helping practitioners to implement safety measures enterprise-wide; analysis, comparison, and evaluation all encompass machine learning, deep learning mechanisms as precautions against potential attacks. We will also consider another study on the services of an AI-provisioned lightweight protocol communication system that ensures data authenticity and responds to diverse security implications in IMD. The chapter makes a U-turn by alluding to internet wearable devices, pointing out federated learning and blockchain reinforcements that indicate the need for an integrated framework. The amalgamation of future technologies supports the ethical and secure integration of IoMT. Collaboration, as a guiding principle, compels healthcare practitioners and policymakers to partner efficiently to guide the future where the security of health is ever easier. In the end, a summary of modern ethical and regulatory paradigms in IoMT is provided, emphasizing that there could be an urgent need for new laws.
Keywords:
Internet of Medical Things; Internet of Healthcare Things; Federated Learning; Artificial Intelligence; Cybersecurity; Intrusion Detection System; ASCP-IoMT; Communication protocol Implantable; Internet Wearable Devices; Security protocols
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0