Human Digital Twin: Data, Models, Applications, and Challenges

TL;DR

This paper reviews the development and application of Human Digital Twins, virtual models of individuals that integrate multimodal data for personalized healthcare, highlighting recent advances and ongoing challenges.

Contribution

It provides a comprehensive overview of modeling techniques, data integration, and challenges in deploying HDTs for precision medicine.

Findings

Advances in anomaly detection and failure prediction for HDTs

Discussion of ethical, technological, and regulatory challenges

Integration of clinical, physiological, behavioral, and environmental data

Abstract

Human digital twins (HDTs) are dynamic, data-driven virtual representations of individuals, continuously updated with multimodal data to simulate, monitor, and predict health trajectories. By integrating clinical, physiological, behavioral, and environmental inputs, HDTs enable personalized diagnostics, treatment planning, and anomaly detection. This paper reviews current approaches to HDT modeling, with a focus on statistical and machine learning techniques, including recent advances in anomaly detection and failure prediction. It also discusses data integration, computational methods, and ethical, technological, and regulatory challenges in deploying HDTs for precision healthcare.