Abstract: With the rapid advancement of technologies such as artificial intelligence (AI), cloud computing, and 5G, the application of digital twins within the smart healthcare domain has become increasingly widespread. This growth has led to the emergence and development of medical digital twins. Patients are at the core of medical digital twin research; however, studying them presents significant challenges. Therefore, this study conducted a detailed investigation of national policy orientations, academic research trends, and international organizations related to medical digital twins. In recent years, numerous governments have placed great emphasis on medical digital twins, including legislation on remote healthcare services in America, mobile healthcare in England, AI hospitals in Japan, and technology-enabled smart healthcare in China. Moreover, this paper proposes a patient-centric comprehensive technical framework for medical digital twins, with a detailed analysis of three key technologies: digital support, digital twin construction, and human–computer interaction. The Internet of Things (IoT) and 5G constitute digital support technologies that underpin the connection between physical space and cyberspace. Devices such as sensors and smart terminals in IoT enable information collection and state perception in the physical world. 5G transmission technology ensures rapid and accurate transmission of collected data. Together, AI, big data, and cloud computing technologies form the core of digital twin construction technology. Cloud computing provides computing power and storage support for big data operations, and massive amounts of data collected by IoT terminal sensors are stored on cloud platforms. AI algorithms, such as machine and deep learning, further enhance the data processing capabilities and system optimization levels of digital twins. In addition, as key components of human–computer interaction technology, VR and AR technologies play significant roles at the application level of digital twins. Next, this paper examines five primary applications of medical digital twins. For instance, in sports medicine, digital twin technology is used to optimize the performance of elite athletes. In disease prediction, recent research has shown that integrating environmental and medical data in digital twin models can significantly improve the prediction accuracy for cancer and infectious diseases. Finally, this paper discusses the challenges of applying digital twin technology in the smart healthcare domain, including data heterogeneity, empiricism, security and privacy, ethics and morality, and the digital divide. For example, questions regarding who is responsible when an AI-based digital twin system makes an incorrect diagnosis must be addressed. To tackle data heterogeneity, ontology technology is being developed to enable seamless data integration. To counter the overreliance on data-driven decision-making, hybrid models that combine AI with clinical guidelines and expert knowledge are being explored. For security and privacy, technologies such as blockchain and federated learning have been proposed to protect patient data, ensuring that only authorized parties can access and process it. In summary, this paper contributes to the theoretical foundation and practical implementation of digital twin technology in smart healthcare, propelling the development of a more efficient, accessible, and personalized medical service ecosystem.