28 Jun 2024 | Jiayuan Chen, You Shi, Changyan Yi, Member, IEEE, Hongyang Du, Jiawen Kang, Dusit Niyato, Fellow, IEEE
This paper provides a comprehensive survey of the implementation of generative artificial intelligence (GAI) in human digital twins (HDT) for IoT-healthcare. HDT is a novel paradigm that aims to create a digital replica of an individual's body, reflecting its physical status in real-time. GAI, with its advanced algorithms, can generate and manipulate valuable and diverse data, making it a promising solution for high-fidelity virtual modeling and information interaction in HDT. The survey covers the background of IoT-healthcare and the potential of GAI-driven HDT, delves into the fundamental techniques and overall framework, and explores the realization of GAI-driven HDT in detail, including data acquisition, communication, data management, digital modeling, and data analysis. It also discusses typical IoT-healthcare applications that can be revolutionized by GAI-driven HDT, such as personalized health monitoring, diagnosis, prescription, and rehabilitation. Finally, the paper highlights future research directions in this field.This paper provides a comprehensive survey of the implementation of generative artificial intelligence (GAI) in human digital twins (HDT) for IoT-healthcare. HDT is a novel paradigm that aims to create a digital replica of an individual's body, reflecting its physical status in real-time. GAI, with its advanced algorithms, can generate and manipulate valuable and diverse data, making it a promising solution for high-fidelity virtual modeling and information interaction in HDT. The survey covers the background of IoT-healthcare and the potential of GAI-driven HDT, delves into the fundamental techniques and overall framework, and explores the realization of GAI-driven HDT in detail, including data acquisition, communication, data management, digital modeling, and data analysis. It also discusses typical IoT-healthcare applications that can be revolutionized by GAI-driven HDT, such as personalized health monitoring, diagnosis, prescription, and rehabilitation. Finally, the paper highlights future research directions in this field.