Generative AI-Driven Human Digital Twin in IoT-Healthcare: A
  Comprehensive Survey
Jiayuan Chen You Shi Changyan Yi Hongyang Du Jiawen Kang Dusit Niyato
The Internet of things (IoT) can significantly enhance the quality of human
life, specifically in healthcare, attracting extensive attentions to
IoT-healthcare services. Meanwhile, the human digital twin (HDT) is proposed as
an innovative paradigm that can comprehensively characterize the replication of
the individual human body in the digital world and reflect its physical status
in real time. Naturally, HDT is envisioned to empower IoT-healthcare beyond the
application of healthcare monitoring by acting as a versatile and vivid human
digital testbed, simulating the outcomes and guiding the practical treatments.
However, successfully establishing HDT requires high-fidelity virtual modeling
and strong information interactions but possibly with scarce, biased and noisy
data. Fortunately, a recent popular technology called generative artificial
intelligence (GAI) may be a promising solution because it can leverage advanced
AI algorithms to automatically create, manipulate, and modify valuable while
diverse data. This survey particularly focuses on the implementation of
GAI-driven HDT in IoT-healthcare. We start by introducing the background of
IoT-healthcare and the potential of GAI-driven HDT. Then, we delve into the
fundamental techniques and present the overall framework of GAI-driven HDT.
After that, we explore the realization of GAI-driven HDT in detail, including
GAI-enabled data acquisition, communication, data management, digital modeling,
and data analysis. Besides, we discuss typical IoT-healthcare applications that
can be revolutionized by GAI-driven HDT, namely personalized health monitoring
and diagnosis, personalized prescription, and personalized rehabilitation.
Finally, we conclude this survey by highlighting some future research
directions.