Medical digital twins (MDTs) are emerging as a transformative tool in personalized medicine, offering the potential to improve patient-specific treatments and diagnostics. This review discusses the current state of MDT development, particularly in oncology and cardiology, and highlights major challenges such as data integration, privacy, and the need for robust modeling technologies. MDTs are defined as computational models that simulate a patient's biological systems and are continuously updated with patient data. They have the potential to enhance clinical decision-making by providing actionable insights and enabling more effective interventions. In oncology, MDTs are used to simulate tumor behavior and assess treatment strategies, with applications in predicting cancer progression and response to therapies. In cardiology, MDTs are used to predict arrhythmia risk and guide treatment decisions, such as in the management of sudden cardiac death. These models integrate data from imaging, electronic health records, and genetic information to create personalized simulations of patient physiology. MDTs face significant challenges, including the biological heterogeneity of patients, the need for high-resolution models, and the lack of standardized data and regulatory frameworks. Despite these challenges, MDTs offer opportunities to improve healthcare outcomes by enabling more precise and personalized treatment strategies. The development of MDTs is supported by advances in computational modeling, machine learning, and data integration, and is being explored in various fields, including infectious disease management and drug development. The future of MDTs depends on overcoming technical, medical, and regulatory challenges, including the need for robust data infrastructure, ethical considerations, and the development of standardized models. As MDT technology continues to evolve, it has the potential to revolutionize healthcare by enabling more effective, personalized, and preventive approaches to patient care.Medical digital twins (MDTs) are emerging as a transformative tool in personalized medicine, offering the potential to improve patient-specific treatments and diagnostics. This review discusses the current state of MDT development, particularly in oncology and cardiology, and highlights major challenges such as data integration, privacy, and the need for robust modeling technologies. MDTs are defined as computational models that simulate a patient's biological systems and are continuously updated with patient data. They have the potential to enhance clinical decision-making by providing actionable insights and enabling more effective interventions. In oncology, MDTs are used to simulate tumor behavior and assess treatment strategies, with applications in predicting cancer progression and response to therapies. In cardiology, MDTs are used to predict arrhythmia risk and guide treatment decisions, such as in the management of sudden cardiac death. These models integrate data from imaging, electronic health records, and genetic information to create personalized simulations of patient physiology. MDTs face significant challenges, including the biological heterogeneity of patients, the need for high-resolution models, and the lack of standardized data and regulatory frameworks. Despite these challenges, MDTs offer opportunities to improve healthcare outcomes by enabling more precise and personalized treatment strategies. The development of MDTs is supported by advances in computational modeling, machine learning, and data integration, and is being explored in various fields, including infectious disease management and drug development. The future of MDTs depends on overcoming technical, medical, and regulatory challenges, including the need for robust data infrastructure, ethical considerations, and the development of standardized models. As MDT technology continues to evolve, it has the potential to revolutionize healthcare by enabling more effective, personalized, and preventive approaches to patient care.