IoT-Enabled Digital Twins for Personalized Healthcare: Real-Time AI Models for Predictive Health and Targeted Treatment

Authors: Subhasis Kundu

DOI: https://doi.org/10.5281/zenodo.15086790

Short DOI: https://doi.org/g89tm3

Country: United States

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Abstract: This study examines the incorporation of Internet of Things (IoT) devices and digital twin technology in personalized healthcare. It explores how artificial intelligence models can leverage continuous sensor data in real-time to create precise digital representations of patients. This research also delves into the potential applications of these digital twins for early disease identification, health prediction analysis, and tailored treatment strategies. This paper presents a framework for deploying IoT-enabled digital twins in medical environments while also discussing the challenges and opportunities associated with this technology. Additionally, the study addresses ethical considerations and privacy concerns related to the use of digital twins in healthcare. The paper concludes by providing case studies that showcase the successful implementation of this technology in various medical contexts and outlines future research directions in this rapidly advancing field.

Keywords: Internet of Things, Digital Twins, Personalized Healthcare, Artificial Intelligence, Predictive Health, Sensor Data, Targeted Treatment, Machine Learning, Data Privacy, Ethical Considerations

Paper Id: 232289

Published On: 2020-11-25

Published In: Volume 8, Issue 6, November-December 2020

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