mlsanityData to 7 Oct 2026

Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey

arXiv 2401.13699 ↗First posted 22 Jan 2024cs.HC cs.AI cs.LGcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 72 of its 196 references that are in the map. First posted on arXiv 22 Jan 2024.

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72 of 196 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 6; 2026: 3.

11 papers.

  1. 2025Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Artcited by 13
  2. 2024Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approachcited by 7
  3. 2025Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuningcited by 7
  4. 2025"X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systemscited by 7
  5. 2025Diffusion Models for Future Networks and Communications: A Comprehensive Surveycited by 6
  6. 2025Generative Artificial Intelligence for Internet of Things Computing: A Systematic Surveycited by 3
  7. 2025Future You: Designing and Evaluating Multimodal AI-generated Digital Twins for Strengthening Future Self-Continuitycited by 2
  8. 2024Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systemscited by 1
  9. 2026Proof of Reasoning for Privacy Enhanced Federated Blockchain Learning at the Edgecited by 1
  10. 2026CalTwin: Towards Calibrated, Shift-Robust Medical World Models via Fisher-Information Regularisationcited by 1
  11. 2026PsyAgent: Constructing Human-like Agents Based on Psychological Modeling and Contextual Interactioncited by 0