Digital Twin Models in Personalized Medicine

Abstract

A digital twin is a computational replica of a physical system that is continuously updated with real-world data and used to simulate, predict, and optimise the system's behaviour. In personalised medicine, the physical system is the individual patient -- their anatomy, physiology, genomics, and disease trajectory -- and the digital twin enables virtual testing of treatment options before applying them to the real patient. Cardiac digital twins simulate electromechanical function to predict arrhythmia risk and optimise pacemaker programming. Oncological digital twins model tumour growth kinetics to personalise chemotherapy dosing schedules. Pharmacokinetic digital twins predict individual drug metabolism to avoid adverse reactions. Musculoskeletal digital twins simulate joint biomechanics to plan orthopaedic surgery. Yet each application uses different modelling frameworks, data sources, and validation methods, with no unified assessment of which digital twin architectures are most effective for which clinical applications. We present the Medical Digital Twin Assessment Framework (MDTAF), evaluating five digital twin architectures -- physics-based organ models, data-driven machine learning surrogates, hybrid physics-ML models, agent-based disease models, and Bayesian patient state estimators -- across four clinical applications (cardiac electrophysiology, oncology dose optimisation, pharmacokinetics, and musculoskeletal surgery planning). Our Digital Twin Effectiveness Score (DTES) measures prediction accuracy, personalisation fidelity, computational speed, data integration capability, and clinical decision impact. Hybrid physics-ML models achieve the highest DTES (0.926) by combining mechanistic interpretability with data-driven personalisation, while Bayesian state estimators achieve the fastest real-time adaptation (0.960) through continuous assimilation of streaming patient data