Digital Twins in Biomedical Engineering

A Systematic Review of Applications and Future Challenges

Abstract

Digital twins (DTs) are a part of computational biomedical engineering, which allows us to come up with real-time virtual copies of biological systems, medical equipment, and clinical procedures. With the help of patient-based data, sensor signals, and modern modeling methods, DTs are opening up huge avenues for personalized treatment and early diagnosis. The methodology for this study involves a systematic literature review and manual thematic synthesis in order to find out the key patterns and insights relevant to the topic. The review followed the Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) 2020 guidelines and consists of those articles that are peer-reviewed and published in English during the time frame of 2015–2026. Global scientific databases such as Google Scholar, Scopus, Web of Science, PubMed, and IEEE Xplore were used. The comprehensive review indicates that the largest part of the Digital Twins applications is related to the areas of cardiovascular modeling, oncology, orthopedics, surgical planning, and chronic disease management. Most of the implementations are a combination of physiological models with machine learning algorithms and patient data from sensors. From a business, economic, and healthcare management perspective, DTs support data-driven decision-making, operational efficiency, cost optimization, and strategic planning in healthcare organizations, contributing to value creation, economic efficiency, and sustainable health systems. Challenges related to computational complexity, model interpretability, data integration, ethical governance, and regulatory approval still persist. The application of DTs leads to a complete transformation in the areas of biomedical engineering, clinical decisionmaking, and sustainable health systems, thus providing patient-oriented, data-informed, and economically efficient healthcare solutions. Future research should prioritize explainable modeling, longitudinal validation, and interdisciplinary collaboration to enable safe and effective adoption.