A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGC

TL;DR

This paper discusses how mobile AI-generated content technology can enable human digital twins to revolutionize personalized healthcare through data generation, modeling, and customized medical services, supported by system architecture and experimental validation.

Contribution

It proposes a novel system architecture for mobile AIGC-driven human digital twins and demonstrates their application in personalized healthcare scenarios.

Findings

Effective virtual physical therapy platform demonstrated

Successful use case in customized surgery planning

Enhanced data generation for rare diseases

Abstract

Mobile Artificial Intelligence-Generated Content (AIGC) technology refers to the adoption of AI algorithms deployed at mobile edge networks to automate the information creation process while fulfilling the requirements of end users. Mobile AIGC has recently attracted phenomenal attentions and can be a key enabling technology for an emerging application, called human digital twin (HDT). HDT empowered by the mobile AIGC is expected to revolutionize the personalized healthcare by generating rare disease data, modeling high-fidelity digital twin, building versatile testbeds, and providing 24/7 customized medical services. To promote the development of this new breed of paradigm, in this article, we propose a system architecture of mobile AIGC-driven HDT and highlight the corresponding design requirements and challenges. Moreover, we illustrate two use cases, i.e., mobile AIGC-driven HDT in customized surgery planning and personalized medication. In addition, we conduct an experimental study to prove the effectiveness of the proposed mobile AIGC-driven HDT solution, which shows a particular application in a virtual physical therapy teaching platform. Finally, we conclude this article by briefly discussing several open issues and future directions.