Digital Twins in Healthcare:The Technology That Wants to Clone You — Virtually

Imagine a version of yourself that lives inside a computer — not a video game avatar, but a scientifically precise virtual replica fed by real-time data from your wearable sensors, medical records, and genomic profile. It predicts how your body will respond to a drug before you swallow the first pill. It warns your physician about a cardiac risk two weeks before you feel any symptoms. It runs thousands of “what-if” scenarios on your behalf while you sleep.

This is not science fiction. This is the promise of the digital twin in healthcare, and the research community is treating it as a serious and rapidly advancing field of inquiry. The studies discussed in this blog post were identified through a systematic search strategy conducted in the Web of Science Core Collection and then refined through healthcare-focused screening and exclusion procedures. What was once a concept rooted largely in engineering has now emerged as a major area of interest in the health sciences.

This blog post translates the main insights from that body of literature into a clear and accessible narrative. Whether you are a health management scholar, a clinical informaticist, a hospital administrator, or simply curious about the future of healthcare technology, this overview explains what digital twins are, what they can offer, what challenges limit their implementation, and why this field deserves close attention.

So, What Exactly Is a Digital Twin?

At its core, a digital twin is a virtual representation of a physical entity — an object, a process, a system, or even a person — that is continuously updated with real-time data (Croatti et al., 2020). Unlike a static model or a one-time simulation, a digital twin lives and breathes with its physical counterpart. It mirrors current conditions, simulates future behaviors, and feeds insights back into the real world. Popa et al. (2021) defined it as a “living replica of a physical system” that converges technologies such as AI, IoT, and big data.

A critical distinction often overlooked in the literature is the difference between a digital model, a digital shadow, and a true digital twin. Araghi et al. (2025) argued that conflating these terms creates a conceptual dilemma. A digital model involves manual, one-way data transfer. A digital shadow automates the data flow from the physical to the virtual. A genuine digital twin, however, establishes a bidirectional data loop — it not only monitors the physical entity but also sends feedback to influence it. This distinction matters because most systems marketed as “digital twins” in healthcare today are, in reality, digital shadows at best.

Pellegrino et al. (2025) identified five core functions of digital twins in healthcare through a systematic meta-review: monitoring, simulation, optimization, modeling, and prediction. Emmert-Streib and Yli-Harja (2022) went further, cautioning that the concept suffers from a definitional vacuum in health contexts and urging the community to embed experimental design principles from data science into the framework. In other words, a digital twin is not just a technological tool — it is an epistemological proposition about how we represent and reason about complex living systems.

The Human Digital Twin: Your Virtual Doppelgänger

The most distinctive application in healthcare is the Human Digital Twin (HDT) — a virtual counterpart of an individual patient. Okegbile et al. (2023) positioned the HDT as an emerging technology capable of revolutionizing personalized healthcare services through tools like AI and blockchain. Chen et al. (2024a) mapped out the comprehensive networking architecture required for HDTs, spanning pervasive sensing, on-body communications, tactile internet, semantic communications, multi-access edge computing, and blockchain.

The integration of generative AI has added a new dimension. Chen et al. (2024b) surveyed how diffusion models, GANs, transformers, and variational autoencoders are enriching HDT capabilities in IoT-healthcare, from synthetic data generation to trajectory forecasting. Makarov et al. (2025) demonstrated that large language models can forecast patient health trajectories, developing the Digital Twin-Generative Pretrained Transformer (DT-GPT) model — effectively proving that the same technology powering chatbots can also power clinical prediction engines.

Tudor et al. (2025), in a scoping review, mapped HDT usage patterns and found the field is still maturing, though concrete applications in clinical decision support and patient monitoring are emerging. Lyu et al. (2026) provided perhaps the most comprehensive recent overview, arguing that the traditional passive model of medical services is proving increasingly inadequate as chronic diseases and population aging intensify, and that HDTs offer a path toward integrated, personalized, and proactive health management.

Looking further ahead, Viceconti et al. (2024) proposed the audacious vision of a “Virtual Human Twin” — a systematic digital representation of the entire known human pathophysiology. They framed this as a “moon-shot project” for digital health research, noting that most current efforts remain narrowly focused on specific organ systems or disease pathways. The gap between where we are and where this vision wants to take us is, frankly, enormous — but so is the potential.

The Technology Stack: What Makes It All Work

IoT and Wearable Sensors

Digital twins are only as good as the data that feeds them. The Internet of Things (IoT) and wearable sensor technologies form the foundational data layer. Khan et al. (2022) described how digital twins emerge from the joint usage of cyber-physical systems, IoT, big data, edge computing, AI, and machine learning — bridging Industry 4.0 to Healthcare 4.0. Bozkaya-Aras et al. (2025) proposed a four-layer architecture framework for human activity recognition in healthcare digital twin services, creating digital twins of wearable and portable devices to collect real-time and historical data.

Artificial Intelligence and Machine Learning

AI serves as the cognitive engine of the digital twin. Lv et al. (2024) demonstrated that deep learning algorithms enhance disease recognition accuracy in clinical big data analytics. Sharma and Kaur (2025) developed a hybrid framework integrating mechanistic physiological simulations with deep learning models, validating patient-specific digital twins on the MIMIC-IV dataset. On the diagnostic side, Sarp et al. (2023) applied GANs to chronic wound management, while Abirami and Karthikeyan (2023) built a fuzzy k-nearest neighbor classifier for early Parkinson’s disease detection within a digital twin architecture.

Blockchain and Security

Healthcare data is among the most sensitive information in existence, and the security challenge is non-trivial. Akash and Ferdous (2022) proposed a blockchain-based healthcare digital twin model to address the structural deficiency in data collection and security. Amofa et al. (2024) advanced this with smart contracts for patient digital twins. The security conversation has since evolved toward post-quantum cryptography: Sourav and Ali (2026) developed a lightweight authentication protocol (LAPQ-BDTH) to protect digital twin healthcare systems against future quantum computing threats, and Prajapat et al. (2026) combined quantum encryption with blockchain for consumer-centric digital twin networks.

5G/6G and Edge Computing

Real-time data transmission demands ultra-reliable, low-latency communication infrastructure. Zhang et al. (2020) positioned the healthcare digital twin as a key service of future 6G networks, envisioning virtual replicas of persons that predict health states using IoT and AI. Brahmi et al. (2024) developed innovative digital twin models with 6G functionalities integrating terahertz frequency communication, while Hu et al. (2026) presented the vision of large AI model-enhanced digital twin-driven 6G Healthcare Internet of Everything (IoE). The convergence of 6G and digital twins represents perhaps the most technically ambitious frontier in the field.

Where Are Digital Twins Being Applied in Healthcare?

Personalized Medicine

This is the flagship application. Bruynseels et al. (2018) — the earliest paper in our dataset — recognized that digital twins from engineering provide a conceptual framework for analyzing data-driven healthcare practices and their ethical implications for therapy and personalized medicine. Mackey et al. (2025) proposed Digital Twin Learning Health Systems (DT-LHS) that integrate multimodal biomarkers and adaptive learning loops to personalize pain management. Shamanna et al. (2025) provided a concrete case study showing how digital twin technology improved metabolic outcomes in a patient with polycystic ovary syndrome. The trajectory from conceptual framework to clinical case study in just seven years is remarkable.

Hospital Facility Management

Beyond the patient, digital twins are transforming the physical infrastructure of healthcare delivery. Madubuike and Anumba (2023) criticized the prevailing “if it breaks, we fix it” approach to healthcare facilities and proposed a digital twin-based framework for real-time monitoring. Han et al. (2023) developed a smart hospital operations framework integrating multi-source real-time data for both clinical and non-clinical operations. Moretti et al. (2025) tackled the critical challenge of data integration for space-aware hospital digital twins, using a histopathology laboratory as a case study. For anyone managing a large hospital complex — think city hospitals in Turkey, or NHS trusts in the UK — these applications have immediate practical relevance.

Emergency and Pandemic Response

COVID-19 served as a powerful catalyst. Zheng et al. (2025) evaluated the digital twin’s capacity for real-time monitoring, dynamic decision support, and resource optimization in healthcare emergency management. De Benedictis et al. (2023) proposed a digital twin architecture for social distancing enforcement, while Khan et al. (2023) developed a digital twin-based Internet of Robotic Things for remote health monitoring of COVID-19 patients. Sun et al. (2025) examined the potential through the PPRR (Prevention-Preparedness-Response-Recovery) framework. The lesson from the pandemic literature is clear: when the next crisis arrives, digital twin infrastructure could mean the difference between reactive scrambling and proactive containment.

Elderly Care and Remote Monitoring

Aging populations represent one of the most natural use cases. Liu et al. (2019) — one of the earliest healthcare digital twin papers — proposed a cloud-based framework specifically for elderly healthcare services. Fahim et al. (2023) developed a deep meta-class sequence model for monitoring elderly behavior and daily activities through digital twins. Chang et al. (2025) conducted a controlled trial evaluating an AI and digital twin-based telemedicine system for improving health outcomes among elderly adults in Chinese townships — one of the few studies in the dataset providing empirical evidence from an actual intervention.

Mental Health

An emerging and fascinating frontier. Yücel et al. (2026) proposed MTWIN, a digital twin-inspired mental health monitoring framework integrating machine learning, IoT, and telemedicine components for continuous emotion detection and well-being monitoring. De Maeyer and Renzi (2026) offered a phenomenological analysis of healthcare digital twins through the Jungian archetypes of Shadow and Self — a strikingly interdisciplinary contribution that bridges computer science with depth psychology.

Surgical Planning and Clinical Operations

Silva-Aravena et al. (2025) developed a digital twin and reinforcement learning approach for optimizing MRI scheduling in high-complexity hospitals. Karakra et al. (2025) created HospiT’Win, a system that uses discrete event simulation as a digital twin for real-time monitoring of patient pathways. Appuhamilage et al. (2025) presented a health digital twin framework for optimizing critical care workflows. These applications directly address the operational pain points that hospital administrators face daily.

Medical Education

Zackoff et al. (2024) evaluated the tolerability and acceptability of autonomous immersive virtual reality incorporating digital twin technology for mass training of healthcare providers — with no prior application in onboarding. The results demonstrated that digital twin VR environments can serve as scalable, standardized training platforms. Sosa-Méndez and García-Cena (2023) developed a robotic digital twin as a training platform for rehabilitation personnel, pushing the Health 4.0 vision into the educational domain.

The Ethical Elephant in the Room

Every powerful technology carries ethical weight, and digital twins are no exception. Burr et al. (2026) conducted a systematic thematic analysis of ethical, legal, and social issues (ELSI) associated with healthcare digital twins, identifying practical barriers to adoption alongside fundamental governance questions. Huang et al. (2022) mapped the ethical landscape through a value-sensitive design lens, highlighting concerns around data privacy, autonomy, and justice.

Popa et al. (2021) balanced socio-ethical benefits against socio-ethical risks from a responsible innovation perspective. Braun and Krutzinna (2022) explored the particularly sensitive terrain of using digital twins in health decision-making concerning children — raising profound questions about proxy consent and predictive modeling for minors. Quinones and Puzio (2025) examined digital twins for trans people in healthcare through queer, phenomenological, and bioethical lenses, highlighting how algorithmic representations can reinforce or challenge binary bodily norms.

Chis and Dumitrache (2025) proposed the “Ethical AI Triplet” framework for stress-testing fairness in healthcare digital twins against core medical principles of empathy, equity, and justice. The ethical conversation is no longer an afterthought — it is rapidly becoming a prerequisite for legitimate research and deployment.

Challenges: What Stands in the Way?

The literature is refreshingly honest about the obstacles. Xames and Topcu (2024), in the first systematic literature review of digital twin research in healthcare systems, documented the fragmented state of the field and the urgent need for knowledge consolidation. Ringeval et al. (2025) confirmed this through a meta-review, cataloging implementation challenges including data integration, interoperability, scalability, and validation.

Sharma et al. (2026) analyzed barriers through the Technology-Organization-Environment (TOE) framework, while Xames et al. (2025) used CFIR 2.0 to evaluate implementation challenges — crucially identifying the conceptual and terminological disconnect between engineering and healthcare communities as a significant barrier. When a computer scientist says “digital twin” and a clinician hears something entirely different, collaboration stalls.

Cybersecurity remains a persistent concern. Zhang et al. (2020) demonstrated that vulnerability detection is fundamental to cyber resilience in healthcare digital twins. Wakili et al. (2025) addressed ransomware, data tampering, and unauthorized access vulnerabilities in IoT-based healthcare systems. The uncomfortable truth is that the more interconnected and data-rich a digital twin becomes, the more attractive it becomes as a target.

The Research Landscape: Where Is the Field Heading?

Kim and Lim (2025) used structural topic modeling to map the interconnectivity of digital twin healthcare research themes, showing a field that is rapidly diversifying. Elgammal et al. (2025) reviewed AI-powered applications across health domains — virtual organs, diabetes management, stress management, mental health, virtual clinical trials, and patient trajectory forecasting — painting a picture of broad but uneven progress.

Several clear trajectories are emerging. First, the convergence of large AI models with 6G infrastructure will enable higher-fidelity, lower-latency digital twins (Hu et al., 2026; Wu et al., 2026). Second, quantum-resistant security protocols are becoming a research priority as quantum computing advances threaten current cryptographic foundations (Bera et al., 2025; Sourav & Ali, 2026). Third, federated learning offers a privacy-preserving path to inter-institutional collaboration, allowing hospitals to train shared models without exchanging raw patient data (Joo et al., 2024; Jiang et al., 2025). Fourth, metaverse integration is opening up immersive clinical experiences and virtual medical services (Moztarzadeh et al., 2023; Yang et al., 2025). And fifth, the sustainability lens — economic, environmental, and social — is being systematically applied to digital twin deployments (Xames & Topcu, 2025).

Final Thoughts: Why This Matters Now

Digital twin technology in healthcare is at an inflection point. The exponential growth in publications — from 1 in 2018 to 57 in 2025 — reflects not just academic enthusiasm but a genuine convergence of enabling technologies (IoT, AI, 5G/6G, blockchain) with pressing healthcare needs (aging populations, pandemic preparedness, personalization demands, cost pressures). The conceptual foundations are laid. The technical building blocks are maturing. The ethical conversations are underway.

What remains is the hard work of implementation: bridging the engineering-healthcare disciplinary divide, building interoperable data infrastructures, navigating regulatory landscapes, and — perhaps most importantly — demonstrating clinical value through rigorous empirical studies. The field has produced far more frameworks and architectures than controlled trials or real-world evaluations. That gap is both a limitation and an opportunity.

For health management scholars and practitioners, especially those in countries like Turkey where digital health infrastructure (centralized appointment systems, electronic health records, city hospital complexes) already provides a strong foundation, the digital twin offers a research frontier with enormous untapped potential. The question is no longer whether digital twins will transform healthcare. The question is who will lead that transformation — and how thoughtfully they will do it.

Data Source: Web of Science Core Collection | TI=(“digital twin*”) | 2018–2026

References

Abirami, L., & Karthikeyan, J. (2023). Digital Twin-Based Healthcare System (DTHS) for Earlier Parkinson Disease Identification. IEEE Access, 11. https://doi.org/10.1109/ACCESS.2023.3312278

Akash, S. S., & Ferdous, M. S. (2022). A Blockchain Based System for Healthcare Digital Twin. IEEE Access, 10. https://doi.org/10.1109/ACCESS.2022.3173617

Amofa, S., Xia, Q., Xia, H., Obiri, I. A., Adjei-Arthur, B., Yang, J. C., & Gao, J. B. (2024). Blockchain-secure patient Digital Twin in healthcare using smart contracts. PLOS ONE, 19(1), e0286120. https://doi.org/10.1371/journal.pone.0286120

Appuhamilage, G. D. K. K., Hussain, M., Zaman, M., & Khan, W. A. (2025). A health digital twin framework for discrete event simulation based optimised critical care workflows. NPJ Digital Medicine, 8. https://doi.org/10.1038/s41746-025-01738-4

Araghi, S. N., Liu, Z. Y., Sarkar, A., Louge, T., & Karray, M. H. (2025). Digital Twin’s Anatomy: A Cross-Sector Framework With Healthcare Validation. IEEE Access, 13. https://doi.org/10.1109/ACCESS.2025.3528736

Bera, B., Das, A. K., & Sikdar, B. (2025). Quantum-Resistant Secure Communication Protocol for Digital Twin-Enabled IoT-Based Healthcare. IEEE Transactions on Network Science and Engineering, 12(3). https://doi.org/10.1109/TNSE.2025.3553044

Bozkaya-Aras, E., Onel, T., Eriskin, L., & Karatas, M. (2025). Intelligent human activity recognition for healthcare digital twin. Internet of Things, 30, 101497. https://doi.org/10.1016/j.iot.2025.101497

Brahmi, R., Boujnah, N., & Ejbali, R. (2024). Elaboration of Innovative Digital Twin Models for Healthcare Monitoring With 6G Functionalities. IEEE Access, 12. https://doi.org/10.1109/ACCESS.2024.3439269

Braun, M., & Krutzinna, J. (2022). Digital twins and the ethics of health decision-making concerning children. Patterns, 3(4), 100469. https://doi.org/10.1016/j.patter.2022.100469

Bruynseels, K., de Sio, F. S., & van den Hoven, J. (2018). Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm. Frontiers in Genetics, 9, 31. https://doi.org/10.3389/fgene.2018.00031

Burr, C. D., Qian, S., Winter, P., Chico, T., Smith, C. R., Wagg, D., & Niederer, S. A. (2026). Realising the digital twin: A thematic review of ELSI for digital twins in healthcare. AI & Society. https://doi.org/10.1007/s00146-025-02833-6

Chang, C. Q., Yan, L. M., Qiu, R. Z., et al. (2025). Analysis of health factors on AI and digital twin telemedicine. Digital Health, 11. https://doi.org/10.1177/20552076251393274

Chen, J. Y., Yi, C. Y., Okegbile, S. D., Cai, J., & Shen, X. M. (2024a). Networking Architecture and Key Supporting Technologies for HDT in Personalized Healthcare. IEEE Communications Surveys and Tutorials, 26(1). https://doi.org/10.1109/COMST.2023.3308717

Chen, J. Y., Shi, Y., Yi, C. Y., Du, H. Y., Kang, J. W., & Niyato, D. (2024b). Generative-AI-Driven Human Digital Twin in IoT Healthcare. IEEE Internet of Things Journal, 11(22). https://doi.org/10.1109/JIOT.2024.3421918

Chis, D. I., & Dumitrache, I. (2025). Ethical AI Triplet: A Framework for Stress-Testing Fairness in Digital Twins in Healthcare. Control Engineering and Applied Informatics, 27(1).

Croatti, A., Gabellini, M., Montagna, S., & Ricci, A. (2020). On the Integration of Agents and Digital Twins in Healthcare. Journal of Medical Systems, 44(9), 161. https://doi.org/10.1007/s10916-020-01623-5

De Benedictis, A., Mazzocca, N., Somma, A., & Strigaro, C. (2023). Digital Twins in Healthcare: An Architectural Proposal for Social Distancing. IEEE JBHI, 27(3). https://doi.org/10.1109/JBHI.2022.3205506

De Maeyer, C., & Renzi, B. (2026). The Shadow and the self in digital twins in healthcare as an AI environment. AI & Society. https://doi.org/10.1007/s00146-026-02960-8

Elgammal, Z., Albrijawi, M. T., & Alhajj, R. (2025). Digital twins in healthcare: A review of AI-powered practical applications. Journal of Big Data, 12. https://doi.org/10.1186/s40537-025-01280-w

Emmert-Streib, F., & Yli-Harja, O. (2022). What Is a Digital Twin? Experimental Design for a Data-Centric ML Perspective in Health. IJMS, 23(21), 13149. https://doi.org/10.3390/ijms232113149

Fahim, M., Sharma, V., Hunter, R., & Duong, T. Q. (2023). Healthy Aging: A Deep Meta-Class Sequence Model in Digital Twin. IEEE JTEHM, 11. https://doi.org/10.1109/JTEHM.2023.3274357

Han, Y. L., Li, Y. B., Li, Y. K., Yang, B., & Cao, L. Y. (2023). Digital twinning for smart hospital operations. Technology in Society, 74, 102317. https://doi.org/10.1016/j.techsoc.2023.102317

Hu, H. Y., Song, Z. Y., & Shi, W. Z. (2026). Large AI Model-Enhanced Digital Twin-Driven 6G Healthcare IoE. Electronics, 15(3), 619. https://doi.org/10.3390/electronics15030619

Huang, P. H., Kim, K. H., & Schermer, M. (2022). Ethical Issues of Digital Twins for Personalized Health Care Service. JMIR, 24(1), e33081. https://doi.org/10.2196/33081

Jiang, L. Y., Ming, X. G., & Zhang, X. Y. (2025). DT-DOFL: Digital-Twin-Empowered Decentralized Online Federated Learning for Smart Healthcare. IEEE TCSS, 12(3). https://doi.org/10.1109/TCSS.2025.3559978

Joo, Y., Camacho, D., Boi, B., Esposito, C., & Choi, C. (2024). Blockchain and Federated Learning Empowered Digital Twin for Effective Healthcare. HCIS, 14, 51. https://doi.org/10.22967/HCIS.2024.14.051

Karakra, A., Fontanili, F., Lamine, E., & Lamothe, J. (2025). A novel application of DES as a digital twin for monitoring patient pathways. NMAHINB, 14. https://doi.org/10.1007/s13721-025-00691-2

Khan, S., Arslan, T., & Ratnarajah, T. (2022). Digital Twin Perspective of Fourth Industrial and Healthcare Revolution. IEEE Access, 10. https://doi.org/10.1109/ACCESS.2022.3156062

Khan, S., Ullah, S., Khan, H. U., & Rehman, I. U. (2023). Digital-Twins-Based IoRT for Remote Health Monitoring of COVID-19 Patients. IEEE IoT Journal, 10(14). https://doi.org/10.1109/JIOT.2023.3267171

Kim, E. M., & Lim, Y. (2025). Mapping interconnectivity of digital twin healthcare research themes through STM. Scientific Reports, 15. https://doi.org/10.1038/s41598-025-17517-w

Liu, Y., Zhang, L., Yang, Y., et al. (2019). A Novel Cloud-Based Framework for Elderly Healthcare Using Digital Twin. IEEE Access, 7, 49088–49101. https://doi.org/10.1109/ACCESS.2019.2909828

Lv, Z. H., Guo, J. K., & Lv, H. B. (2024). Deep Learning-Empowered Clinical Big Data Analytics in Healthcare Digital Twins. IEEE/ACM TCBB, 21(2). https://doi.org/10.1109/TCBB.2023.3252668

Lyu, S. H., Lim, K. Y. H., Chen, C. H., Youcef-Toumi, K., & Yang, X. J. (2026). Human digital twins in healthcare and occupational well-being. Computers in Industry, 157, 104463. https://doi.org/10.1016/j.compind.2026.104463

Mackey, S., Darnall, B., & Kao, M. C. (2025). Digital twin learning health systems and multimodal biomarkers transform pain care. Pain, 166(11S), S106–S110. https://doi.org/10.1097/j.pain.0000000000003779

Madubuike, O. C., & Anumba, C. J. (2023). Digital Twin-Based Health Care Facilities Management. J. Computing in Civil Engineering, 37(3). https://doi.org/10.1061/JCCEE5.CPENG-4842

Makarov, N., Bordukova, M., Quengdaeng, P., et al. (2025). Large language models forecast patient health trajectories enabling digital twins. NPJ Digital Medicine, 8. https://doi.org/10.1038/s41746-025-02004-3

Moretti, N., Chan, Y. C., Nakaoka, M., et al. (2025). Data integration for space-aware Digital Twins of hospital operations. Automation in Construction, 175, 106276. https://doi.org/10.1016/j.autcon.2025.106276

Moztarzadeh, O., Jamshidi, M., Sargolzaei, S., et al. (2023). Metaverse and Healthcare: ML-Enabled Digital Twins of Cancer. Bioengineering, 10(4), 455. https://doi.org/10.3390/bioengineering10040455

Okegbile, S. D., Cai, J., Yi, C. Y., & Niyato, D. (2023). Human Digital Twin for Personalized Healthcare: Vision, Architecture and Future Directions. IEEE Network, 37(2). https://doi.org/10.1109/MNET.118.2200071

Pellegrino, G., Gervasi, M., Angelelli, M., & Corallo, A. (2025). A Conceptual Framework for Digital Twin in Healthcare: Evidence from a Systematic Meta-Review. ISF, 27. https://doi.org/10.1007/s10796-024-10536-4

Popa, E. O., van Hilten, M., Oosterkamp, E., & Bogaardt, M. J. (2021). The use of digital twins in healthcare: Socio-ethical benefits and risks. Life Sciences, Society and Policy, 17, 6. https://doi.org/10.1186/s40504-021-00113-x

Prajapat, S., Hwang, S. O., Kumar, P., & Shabaz, M. (2026). A Blockchain-Enabled Quantum Encryption Scheme for Consumer-Centric Digital Twin Healthcare. IEEE TCE, 72(1). https://doi.org/10.1109/TCE.2025.3634583

Quinones, J. L. G., & Puzio, A. (2025). Digital twins for trans people in healthcare: Queer, phenomenological and bioethical considerations. J. Medical Ethics, 51(5). https://doi.org/10.1136/jme-2024-110403

Ringeval, M., Sosso, F. A. E., & Cousineau, M. (2025). Advancing Health Care With Digital Twins: Meta-Review. JMIR, 27, e69544. https://doi.org/10.2196/69544

Sarp, S., Kuzlu, M., Zhao, Y. X., & Gueler, O. (2023). Digital Twin in Healthcare: Chronic Wound Management. IEEE JBHI, 27(11). https://doi.org/10.1109/JBHI.2023.3299028

Shamanna, P., Maheshwari, A., Keshavamurthy, A., et al. (2025). Digital Twin Technology in Resolving PCOS and Improving Metabolic Health. AACE Clinical Case Reports, 11(2). https://doi.org/10.1016/j.aace.2024.11.004

Sharma, A. K., Srivastava, M. K., & Sharma, R. (2026). Barriers and Challenges for Digital Twin Adoption in Healthcare Supply Chain. Global Business Review. https://doi.org/10.1177/09721509251314795

Sharma, H., & Kaur, S. (2025). Patient-Specific Digital Twins for Personalized Healthcare: A Hybrid AI Framework. IEEE Access, 13. https://doi.org/10.1109/ACCESS.2025.3598130

Silva-Aravena, F., Morales, J., Jayabalan, M., & Sáez, P. (2025). Optimizing MRI Scheduling: A Digital Twin and RL Approach. Bioengineering, 12(6), 626. https://doi.org/10.3390/bioengineering12060626

Sosa-Méndez, D., & García-Cena, C. E. (2023). Robotic digital twin as a training platform for rehabilitation health personnel. Enfoque UTE, 14(3). https://doi.org/10.29019/enfoqueute.971

Sourav, & Ali, R. (2026). LAPQ-BDTH: Lightweight Authentication for Postquantum Secure Blockchain in Digital Twin Healthcare. IEEE IoT Journal, 13(5). https://doi.org/10.1109/JIOT.2025.3629806

Sun, T. Z., Lu, J. L., Li, Q. C., & Kou, Y. H. (2025). Can digital twin technology revolutionize public health emergency management? Frontiers in Public Health, 13, 1631339. https://doi.org/10.3389/fpubh.2025.1631339

Tudor, B. H., Shargo, R., Gray, G. M., et al. (2025). A scoping review of human digital twins in healthcare. NPJ Digital Medicine, 8. https://doi.org/10.1038/s41746-025-01910-w

Viceconti, M., De Vos, M., Mellone, S., & Geris, L. (2024). From Digital Twins in Healthcare to the Virtual Human Twin: A Moon-Shot Project. IEEE JBHI, 28(2). https://doi.org/10.1109/JBHI.2023.3323688

Wakili, A., Bakkali, S., & Ibrahim, I. A. (2025). A digital twin-enhanced cybersecurity framework for IoT in healthcare. Telematics and Informatics Reports, 18, 100254. https://doi.org/10.1016/j.teler.2025.100254

Wu, B. N., Huang, J., & Duan, Q. (2026). Real-Time Intelligent Healthcare Enabled by Federated Digital Twins With AoI Optimization. IEEE Network, 40(2). https://doi.org/10.1109/MNET.2025.3565977

Xames, M. D., & Topcu, T. G. (2024). A Systematic Literature Review of Digital Twin Research for Healthcare Systems. IEEE Access, 12. https://doi.org/10.1109/ACCESS.2023.3349379

Xames, M. D., & Topcu, T. G. (2025). How Can Digital Twins Support the Sustainability of Healthcare Systems. IEEE Access, 13. https://doi.org/10.1109/ACCESS.2025.3559502

Xames, M. D., Topcu, T. G., Parker, S. H., Zagarese, V., & Epling, J. W. (2025). Evaluating CFIR 2.0 in identifying digital twin implementation challenges in healthcare. Frontiers in Digital Health, 7, 1611225. https://doi.org/10.3389/fdgth.2025.1611225

Yang, J., Khan, A. A., Yang, X. T., et al. (2025). Toward Secure and Standardized AI Frameworks in Metaverse-Enabled Cognitive Digital Twins. IEEE Communications Standards Magazine, 9(1). https://doi.org/10.1109/MCOMSTD.2025.3629149

Yücel, K., Ozcanhan, M. H., & Ünlütürk, M. S. (2026). MTWIN: A mental health monitoring framework toward a Digital Twin. Smart Health, 36, 100646. https://doi.org/10.1016/j.smhl.2026.100646

Zackoff, M. W., Davis, D., Rios, M., et al. (2024). Tolerability and Acceptability of Autonomous Immersive VR Incorporating Digital Twin for Mass Training in Healthcare. Simulation in Healthcare, 19(4). https://doi.org/10.1097/SIH.0000000000000755

Zhang, J., Li, L., Lin, G. J., et al. (2020). Cyber Resilience in Healthcare Digital Twin on Lung Cancer. IEEE Access, 8, 201900–201913. https://doi.org/10.1109/ACCESS.2020.3034324

Zheng, R. Y., Ng, S. T., Shao, Y. Y., Li, Z. F., & Xing, J. D. (2025). Leveraging digital twin for healthcare emergency management system. RESS, 260, 111079. https://doi.org/10.1016/j.ress.2025.111079