A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGC
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Abstract (arXiv)
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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 7 of its 18 references that are in the map. First posted on arXiv 22 Jul 2023.
Builds on
7 of 18 references.
References · 7
- 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Modelscited by 514
- 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
- 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 251
- 2022Privacy for Free: How does Dataset Condensation Help Privacy?cited by 119
- 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Surveycited by 34
- 2023AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharingcited by 21
- 2019Ultrareliable and Low-Latency Communication Techniques for Tactile Internet Servicescited by 2
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Citing papers per year: 2024: 1; 2025: 4; 2026: 2.
7 papers.
- 2024Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Surveycited by 11
- 2025From Screens to Scenes: A Survey of Embodied AI in Healthcarecited by 10
- 2025Diffusion Models for Future Networks and Communications: A Comprehensive Surveycited by 6
- 2025Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correctioncited by 2
- 2025Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Frameworkcited by 1
- 2026Proof of Reasoning for Privacy Enhanced Federated Blockchain Learning at the Edgecited by 1
- 2026Deep Learning-Based Channel Extrapolation for Dual-Band Massive MIMO Systemscited by 0
- 2026Proof of Reasoning for Privacy Enhanced Federated Blockchain Learning at the Edge12 Jan 2026
- 2026Deep Learning-Based Channel Extrapolation for Dual-Band Massive MIMO Systems11 Jan 2026
- 2025Diffusion Models for Future Networks and Communications: A Comprehensive Survey3 Aug 2025
- 2025Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction17 Jul 2025
- 2025Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework6 Mar 2025
- 2025From Screens to Scenes: A Survey of Embodied AI in Healthcare13 Jan 2025
- 2024Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey22 Jan 2024