Precision Cardio-Oncology: Digital Twins and AI for Personalized Cardiovascular Risk Assessment

Abstract

Background: Cardio-oncology has emerged as a rapidly evolving multidisciplinary specialty focused on preventing, detecting, and managing cardiovascular complications associated with cancer and its treatment. Although remarkable advances in chemotherapy, targeted therapies, immunotherapy, and radiation therapy have significantly improved cancer survival, cardiovascular disease has become one of the leading causes of long-term morbidity and mortality among cancer survivors. The complex interactions among tumor biology, cardiovascular physiology, therapeutic exposure, genetic susceptibility, immune responses, and patient-specific clinical factors necessitate individualized approaches to cardiovascular risk assessment and management. Recent developments in artificial intelligence (AI), digital twin technology, multimodal data integration, and computational medicine have introduced transformative opportunities for precision cardio-oncology. Digital twins are continuously evolving virtual representations of individual patients that integrate cardiac imaging, electrocardiography, genomic sequencing, laboratory biomarkers, wearable devices, electronic health records, cancer treatment history, and longitudinal clinical information to simulate cardiovascular health, predict treatment-related toxicity, and optimize personalized therapeutic strategies. Advances in machine learning, deep learning, transformer architectures, graph neural networks, reinforcement learning, and explainable artificial intelligence have significantly enhanced cardiovascular prediction, early diagnosis, treatment optimization, and survivorship management. Despite remarkable progress, challenges remain regarding data interoperability, computational complexity, ethical governance, cybersecurity, regulatory validation, and large-scale clinical implementation. This review provides a comprehensive overview of artificial intelligence and digital twin technologies in precision cardio-oncology, highlighting computational foundations, clinical applications, emerging innovations, and future perspectives for personalized cardiovascular risk assessment in patients with cancer.