Comparative analysis on AI-driven human digital twin for personalized and predictive medicine

Abstract

The emergence of digital twin technology in medicine signifies a paradigm shift toward personalized healthcare and precision therapeutics. A human digital twin (HDT) is a dynamic, virtual representation of an individual’s physiological state, constructed and continuously updated via real-time data streams from wearable sensors, advanced imaging, and molecular diagnostics. This paper comparatively analyses the capacity of HDTs to enable high-fidelity simulation of patient-specific disease conditions, thereby optimizing therapeutic intervention, surgical planning, and predictive health management. By integrating advancements in artificial intelligence, machine learning, and the Internet of Things (IoT), HDTs facilitate the modeling of complex, multi-scale biological interactions, from genetic and molecular pathways to systemic organ function. We review current computational frameworks for HDT development, address critical challenges including data integration, model interoperability, and validation, and consider the transformative impact on clinical workflows. Concurrently, we examine the imperative ethical considerations and data security protocols required for responsible clinical deployment. The realization of personal digital twins holds the potential to revolutionize medical practice by facilitating truly individualized therapeutic strategies, improving patient outcomes, and accelerating biomedical discovery.

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