Abstract Digital twins (DTs) are rapidly emerging as a transformative technology in health care, offering real-time, data-driven simulations of organs, patients, and hospital systems. These virtual models enhance the accuracy of predictive diagnostics, the effectiveness of personalized therapies, and operational efficiencies; thus, making them a strategic asset for both clinical care and business considerations. While these technologies originated in aerospace and manufacturing, health care applications of DT are accelerating significantly, primarily driven by advances in artificial intelligence (AI), cloud computing, and wearable sensors (e. g. , IoT: Internet of Things). The global market for DT in health care applications is projected to grow from 2. 7 billion in 2024 to nearly 60 billion by 2030, signaling a shift in how health care is delivered, funded, and optimized. This study is focused on developing a structured four-stage framework for translating DT innovation into scalable clinical solutions, from early-stage research and pilot programs to commercialization and full-scale hospital integration. The authors compared platforms established by enterprises with those developed by startups. By exploring a case study of a DT for cardiovascular applications, the role of targeted value propositions, regulatory alignment, and staged funding strategies is illustrated. Key enablers such as “software as a medical device pathway, ” AI, and interoperability standards (e. g. , for IoT) are discussed alongside business model design and stakeholder alignment. The results of this case study can provide a practical roadmap for entrepreneurs, investors, and corporate leaders in the health care sector. This case study can provide a pathway for professionals aiming to capitalize on the convergence of digital health, personalized medicine, and value-based care. These topics related to DT are to fostering innovative organizational strategies and essential for addressing business imperatives that are redefining competitive advantages.
Nisar et al. (Thu,) studied this question.
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