Applications of digital twin in precision medicine: a systematic review

Abstract

Digital twin (DT) technology has emerged as a transformative tool in healthcare and precision medicine, enabling personalized care through real-time simulations, predictive modelling, and patient-specific diagnostics. This systematic review analyses 94 peer-reviewed articles from the Scopus database, highlighting the role of machine learning (ML) techniques—such as supervised, unsupervised, and hybrid methods—in advancing DT applications. The study explores the integration of emerging technologies, including blockchain and generative artificial intelligence (AI), to enhance DT functionality and data security. Spanning disciplines such as cardiology, oncology, neurology, and metabolic disorders, the research underscores the interdisciplinary applications of DTs while addressing critical ethical concerns like data privacy, algorithmic bias, and regulatory challenges. Despite limitations, including reliance on a single database and subjective categorization, this review demonstrates the transformative potential of DTs and suggests future research directions in quantum computing integration and patient-centered DT design