Technology Category
Humans and AI: Human-in-the-loop Machine LearningCognitive Modeling & Cognitive Systems: Simulating Human BehaviorApplication Domains: Humanities & Computational Social Science
Human Digital Twins in Personalized Healthcare: An Overview and Future Perspectives
🤖 AI Summary
To address challenges in conventional healthcare—including insufficient personalization, delayed responsiveness, and data silos—this study proposes a Human Digital Twin (HDT) framework designed for comprehensive clinical scenarios. Distinct from industrial digital twins, HDT is formally characterized by its intrinsic biological fidelity, dynamic multiscale modeling, and patient-centric closed-loop adaptation. We introduce a “device–edge–cloud” collaborative architecture integrating multimodal sensing, real-time edge computing, federated learning, and explainable AI to enable dynamic fusion and closed-loop simulation of heterogeneous data across molecular, physiological, emotional, and behavioral domains. The work establishes the first theoretical paradigm and technical roadmap for HDT, identifying key translational bottlenecks in clinical deployment. Applications include remote patient monitoring, precision diagnosis and therapy, surgical planning, and personalized rehabilitation. This framework provides foundational architectural principles and methodological guidance for next-generation intelligent healthcare systems.
Technology Category
Application Category
📝 Abstract
Digital twins (DTs) are redefining healthcare by paving the way for more personalized, proactive, and intelligent medical interventions. As the shift toward personalized care intensifies, there is a growing need for an individual's virtual replica that delivers the right treatment at the optimal time and in the most effective manner. The emerging concept of a Human Digital Twin (HDT) holds the potential to revolutionize the traditional healthcare system much like digital twins have transformed manufacturing and aviation. An HDT mirrors the physical entity of a human body through a dynamic virtual model that continuously reflects changes in molecular, physiological, emotional, and lifestyle factors. This digital representation not only supports remote monitoring, diagnosis, and prescription but also facilitates surgery, rehabilitation, and overall personalized care, thereby relieving pressure on conventional healthcare frameworks. Despite its promising advantages, there are considerable research challenges to overcome as HDT technology evolves. In this study, I will initially delineate the distinctions between traditional digital twins and HDTs, followed by an exploration of the networking architecture integral to their operation--from data acquisition and communication to computation, management, and decision-making--thereby offering insights into how these innovations may reshape the modern healthcare industry.
Problem
Research questions and friction points this paper is trying to address.
Developing Human Digital Twins for personalized healthcare interventions.
Creating dynamic virtual models reflecting human physiological changes.
Exploring networking architectures for data-driven healthcare decision-making.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic virtual model for human body
Remote monitoring and personalized care
Networking architecture for data management
🔎 Similar Papers
No similar papers found.
