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Review Article

Amalgamation of Transfer Learning and Explainable AI for Internet of Medical Things

Author(s): Ramalingam Murugan*, Manish Paliwal, Rama Seetha Maha Lakshmi Patibandla, Pooja Shah, Tarakeswara Rao Balaga, Deepti Raj Gurrammagari*, Parvathavarthini Singaravelu, Gokul Yenduri* and Rutvij Jhaveri

Volume 17, Issue 4, 2024

Published on: 19 December, 2023

Article ID: e191223224674 Pages: 14

DOI: 10.2174/0126662558285074231120063921

Price: $65

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Abstract

The Internet of Medical Things (IoMT), a growing field, involves the interconnection of medical devices and data sources. It connects smart devices with data and optimizes patient data with real time insights and personalized solutions. It is mandatory to hold the development of IoMT and join the evolution of healthcare. This integration of Transfer Learning and Explainable AI for IoMT is considered to be an essential advancement in healthcare. By making use of knowledge transfer between medical domains, Transfer Learning enhances diagnostic accuracy while reducing data necessities. This makes IoMT applications more efficient which is considered to be a mandate in today’s healthcare. In addition, explainable AI techniques offer transparency and interpretability to AI driven medical decisions. This can foster trust among healthcare professionals and patients. This integration empowers personalized medicine, supports clinical decision making, and confirms the responsible handling of sensitive patient data. Therefore, this integration promises to revolutionize healthcare by merging the strengths of AI driven insights with the requirement for understandable, trustworthy, and adaptable systems in the IoMT ecosystem.

Graphical Abstract

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