Digital twins in healthcare: a structured review with emphasis on cancer care and disease modeling

Abstract

Digital Twin (DT) technology has emerged as a promising paradigm for enhancing healthcare systems by enabling real-time, data-driven virtual representations of patients, clinical processes and medical environments. Driven by advances in the Internet of Things, artificial intelligence, cloud computing and extended reality, DTs offer new opportunities for personalized care, predictive analytics, and operational optimization. This paper presents a comprehensive review of Digital Twin applications in healthcare, systematically analyzing recent research across four major domains: therapy and rehabilitation, hospital operations and patient flow management, personalized and precision medicine and cancer care and disease modeling. Particular emphasis is placed on cancer-related Digital Twins due to the complexity, cost and critical nature of oncology, where DTs demonstrate strong potential for early detection, treatment planning and disease progression modeling. The review further examines key enabling technologies, identifies current challenges and open research issues and outlines future research directions to support the development of robust, scalable and clinically validated healthcare Digital Twin systems. This work aims to provide researchers and practitioners with a structured understanding of the current state, limitations and future potential of Digital Twin technology in healthcare.