The health digital twin: advancing precision cardiovascular medicine

Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm

Bruynseels, K., Santoni de Sio, F., & van den Hoven, J. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00031 · Cited by 704 · see citing works

Digital Twins: From Personalised Medicine to Precision Public Health

Kamel Boulos, M. N., & Zhang, P. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11080745 · Cited by 530 · see citing works

ST‐Segment–Elevation Myocardial Infarction (STEMI) Patients Without Standard Modifiable Cardiovascular Risk Factors—How Common Are They, and What Are Their Outcomes?

Vernon, S. T., Coffey, S., D'Souza, M. I., Chow, C. K., Kilian, J. G., Hyun, K. K., Shaw, J. A., Adams, M., Roberts‐Thomson, P., Brieger, D. B., et al. (2019) · Journal of the American Heart Association · doi:10.1161/jaha.119.013296 · Cited by 203 · see citing works

Cardio Twin: A Digital Twin of the human heart running on the edge

Velazquez, R., Díaz, R. G., & Saddik, A. E. (2019) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea.2019.8802162 · Cited by 170 · see citing works

Precision medicine: Beyond the inflection point

Hawgood, S., Hook-Barnard, I. G., O’Brien, T. C., & Yamamoto, K. R. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aaa9970 · Cited by 112 · see citing works

Machine Meets Biology: a Primer on Artificial Intelligence in Cardiology and Cardiac Imaging

Dilsizian, M. E., & Siegel, E. L. (2018) · Current Cardiology Reports · doi:10.1007/s11886-018-1074-8 · Cited by 54 · see citing works

An in silico twin for epicardial augmentation of the failing heart

Hirschvogel, M., Jagschies, L., Maier, A., Wildhirt, S. M., & Gee, M. W. (2019) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.3233 · Cited by 40 · see citing works

Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database

Jones, G., Parr, J., Nithiarasu, P., & Pant, S. (2021) · Biomechanics and Modeling in Mechanobiology · doi:10.1007/s10237-021-01497-7 · Cited by 31 · see citing works

The future of precision medicine in Australia

Williamson, R. A., Anderson, W., Duckett, S. J., Frazer, I. H., Hillyard, C., Kowal, E., Mattick, J. S., McLean, C., North, K. N., Turner, A. K., et al. (2018) · Figshare · Cited by 13 · see citing works

Multimodal biomedical AI

Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365 · see citing works

The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field

Coorey, G., Figtree, G. A., Fletcher, D. F., Snelson, V. J., Vernon, S. T., Winlaw, D. S., Grieve, S. M., McEwan, A. L., Yang, J. Y. H., Qian, P. C., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00640-7 · Cited by 415 · see citing works

Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice

Khera, R., Oikonomou, E. K., Nadkarni, G. N., Morley, J., Wiens, J., Butte, A. J., & Topol, E. J. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.05.003 · Cited by 309 · see citing works

Advancements and challenges of digital twins in industry

Tao, F., Zhang, H., & Zhang, C. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00603-w · Cited by 286 · see citing works

Impactful Digital Twin in the Healthcare Revolution

Hassani, H., Huang, X., & MacFeely, S. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6030083 · Cited by 200 · see citing works

Challenges and opportunities for digital twins in precision medicine from a complex systems perspective

Domenico, M. D., Allegri, L., Caldarelli, G., d’Andrea, V., Camillo, B. D., Rocha, L. M., Rozum, J. C., Sbarbati, R., & Zambelli, F. (2025) · npj Digital Medicine · doi:10.1038/s41746-024-01402-3 · Cited by 184 · see citing works

The Bifold Triadic Relationships Framework: A Theoretical Primer for Advertising Research in the Metaverse

Ahn, S. J., Kim, J., & Kim, J. (2022) · Journal of Advertising · doi:10.1080/00913367.2022.2111729 · Cited by 164 · see citing works

Concepts and applications of digital twins in healthcare and medicine

Zhang, K., Zhou, H.-Y., Baptista‐Hon, D. T., Gao, Y., Liu, X., Oermann, E. K., Xu, S. H., Jin, S., Zhang, J., Sun, Z., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101028 · Cited by 170 · see citing works

Digital twin-supported smart city: Status, challenges and future research directions

Wang, H., Chen, X., Jia, F., & Cheng, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119531 · Cited by 135 · see citing works

Robust triboelectric information‐mat enhanced by multi‐modality deep learning for smart home

Yang, Y., Shi, Q., Zhang, Z., Shan, X., Salam, B., & Lee, C. (2022) · InfoMat · doi:10.1002/inf2.12360 · Cited by 84 · see citing works

Literature review of digital twin in healthcare

Machado, T. M., & Berssaneti, F. T. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e19390 · Cited by 73 · see citing works

Digital-Twin-Enabled IoMT System for Surgical Simulation Using rAC-GAN

Tai, Y., Zhang, L., Li, Q., Zhu, C., Chang, V. I. C., Rodrigues, J. J. P. C., & Guizani, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3176300 · Cited by 70 · see citing works

Hybrid monitoring methodology: A model-data integrated digital twin framework for structural health monitoring and full-field virtual sensing

Sun, L., Sun, H., Zhang, W., & Li, Y. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102386 · Cited by 69 · see citing works

The transition from genomics to phenomics in personalized population health

Yurkovich, J. T., Evans, S. J., Rappaport, N., Boore, J. L., Lovejoy, J. C., Price, N. D., & Hood, L. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00674-x · Cited by 61 · see citing works

Intelligent Digital Twins for Personalized Migraine Care

Gazerani, P. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13081255 · Cited by 61 · see citing works

Enabling Digital Twins to Support the UN SDGs

Hassani, H., Huang, X., & MacFeely, S. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6040115 · Cited by 55 · see citing works

Electrical Impedance Tomography Guided by Digital Twins and Deep Learning for Lung Monitoring

Zhu, L., Lu, W., Soleimani, M., Li, Z., & Zhang, M. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3298389 · Cited by 51 · see citing works

Digital twins in healthcare IoT: A systematic review

Kabir, M. R., Shishir, F. S., Shomaji, S., & Ray, S. (2025) · High Confid. Comput. · doi:10.1016/j.hcc.2025.100340 · Cited by 61 · see citing works

Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning

Hong, G.-S., Jang, M., Kyung, S., Cho, K., Jeong, J., Lee, G. Y., Shin, K., Kim, K., Ryu, S. M., Seo, J. B., et al. (2023) · Korean Journal of Radiology · doi:10.3348/kjr.2023.0393 · Cited by 45 · see citing works

Digital Twin Application: Making a Virtual Pig House Toward Digital Livestock Farming

Jeong, D., Jo, S., Lee, I.-B., Shin, H., & Kim, J. (2023) · IEEE Access · doi:10.1109/access.2023.3313618 · Cited by 40 · see citing works

The Digital Twin: A Potential Solution for the Personalized Diagnosis and Treatment of Musculoskeletal System Diseases

Sun, T., Wang, J., Suo, M., Liu, X., Huang, H., Zhang, J., Zhang, W., & Li, Z. (2023) · Bioengineering · doi:10.3390/bioengineering10060627 · Cited by 42 · see citing works

Digital twins in healthcare: Applications, technologies, simulations, and future trends

Elaziz, M. A., Al‐qaness, M. A. A., Dahou, A., Al‐Betar, M. A., Mohamed, M. M., El‐Shinawi, M., Ali, A., & Ewees, A. A. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1559 · Cited by 37 · see citing works

What Is a Digital Twin? Experimental Design for a Data-Centric Machine Learning Perspective in Health

Emmert-Streib, F., & Yli-Harja, O. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232113149 · Cited by 33 · see citing works

Longitudinal big biological data in the AI era

Mardinoğlu, A., Türkez, H., Shong, M., Srinivasulu, V. P., Nielsen, J., Palsson, B. Ø., Hood, L., & Uhlén, M. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00134-0 · Cited by 35 · see citing works

Generative AI in Academic Writing: A Comparison of DeepSeek, Qwen, ChatGPT, Gemini, Llama, Mistral, and Gemma

Aydın, Ö., Karaarslan, E., Erenay, F. S., & Bačanin, N. (2026) · Turkish Journal of Engineering · doi:10.31127/tuje.1838765 · Cited by 55 · see citing works

Computational Modelling and Simulation of Fluid Structure Interaction in Aortic Aneurysms: A Systematic Review and Discussion of the Clinical Potential

Mourato, A., Valente, R., Xavier, J., Brito, M., Avril, S., De, J., Tomás, A. C., & Fragata, J. (2022) · Applied Sciences · doi:10.3390/app12168049 · Cited by 29 · see citing works

SIGNED: Smart cIty diGital twiN vErifiable Data Framework

Pervez, Z., Khan, Z., Abbasi, A. G., & Soomro, K. (2023) · IEEE Access · doi:10.1109/access.2023.3260621 · Cited by 28 · see citing works

Digital twins in healthcare and biomedicine

Subaşı, A., & Subasi, M. E. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-21598-8.00011-7 · Cited by 26 · see citing works

DECIDE-Twin: A Framework for AI-Enabled Digital Twins in Clinical Decision-Making

Rahimi, S. A., Baradaran, A., Khameneifar, F., Gore, G. C., & Issa, A. M. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3521717 · Cited by 29 · see citing works

Characterizing the Role of Geospatial Science in Digital Twins

Metcalfe, J. B., Ellul, C., Morley, J. G., & Stoter, J. (2024) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi13090320 · Cited by 26 · see citing works

Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling

Phillips, N. E., Collet, T., & Naef, F. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100545 · Cited by 20 · see citing works

The role of digital twins in P4 medicine: A paradigm for modern healthcare

Emmert‐Streib, F., Parkkila, S., Laubenbacher, R., Mannermaa, A., Hood, L., & Yli-Harja, O. P. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02115-x · Cited by 19 · see citing works

Combining experimental and computational techniques to understand and improve dry powder inhalers

Chaugule, V., Wong, C. Y. J., Inthavong, K., Fletcher, D. F., Young, P. M., Soria, J., & Traini, D. (2022) · Expert Opinion on Drug Delivery · doi:10.1080/17425247.2022.2026922 · Cited by 19 · see citing works

Measuring pain and nociception: Through the glasses of a computational scientist. Transdisciplinary overview of methods

Kutafina, E., Becker, S., & Namer, B. (2023) · Frontiers in Network Physiology · doi:10.3389/fnetp.2023.1099282 · Cited by 19 · see citing works

A systematic review of Digital Twins in efficient pandemic management with challenges and emerging trends

Eumi, E. M. (2024) · Decision Analytics Journal · doi:10.1016/j.dajour.2024.100502 · Cited by 17 · see citing works

Current state-of-the-art and utilities of machine learning for detection, monitoring, growth prediction, rupture risk assessment, and post-surgical management of abdominal aortic aneurysms

Baek, S., & Arzani, A. (2022) · Applications in Engineering Science · doi:10.1016/j.apples.2022.100097 · Cited by 17 · see citing works

Control of medical digital twins with artificial neural networks

Böttcher, L., Fonseca, L. L., & Laubenbacher, R. (2025) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2024.0228 · Cited by 21 · see citing works

Optimal control of agent-based models via surrogate modeling

Fonseca, L. L., Böttcher, L., Mehrad, B., & Laubenbacher, R. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012138 · Cited by 17 · see citing works

Continuous biomanufacturing in upstream and downstream processing

Schmidt, A., Hengelbrock, A., & Strube, J. (2023) · Physical Sciences Reviews · doi:10.1515/psr-2022-0106 · Cited by 13 · see citing works

Digital Twins and Healthcare: Quick Overview and Human-Centric Perspectives

Barresi, G., Gaggioli, A., Sternini, F., Ravizza, A., Pacchierotti, C., & Michieli, L. D. (2023) · Studies in big data · doi:10.1007/978-981-99-3989-3_4 · Cited by 11 · see citing works

Closing the loop: autonomous intelligent control for hypoxia pre-acclimatization and high-altitude health management

Shi, D., Chen, J., Li, M., Zhu, L., & Ji, X. (2025) · National Science Review · doi:10.1093/nsr/nwaf071 · Cited by 11 · see citing works

Digital Twins in Medicine, AI-Driven Personalized Healthcare, and Predictive Analytics

Sen, S., & Kılıç, K. D. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0538-7.ch011 · Cited by 8 · see citing works

DDD-GenDT: Dynamic Data-Driven Generative Digital Twin Framework

Lin, Y.-Z., Shi, Q., Yang, Z., Latibari, B. S., Satam, S., Shao, S., Salehi, S., & Satam, P. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3612920 · Cited by 10 · see citing works

Synthetic Data Digital Twins and Data Trusts Control for Privacy in Health Data Sharing

Lomotey, R. K., Kumi, S., Ray, M., & Deters, R. (2024) · ACM Workshop on Secure and Trustworthy Cyber-Physical Systems (SaT-CPS) · doi:10.1145/3643650.3658605 · Cited by 7 · see citing works

Artificial intelligence in cardiovascular prognosis and diagnosis: a review

Bayona, A. V., Wang, J., & Xiang, Y. (2025) · Exploration of Medicine · doi:10.37349/emed.2025.1001347 · Cited by 7 · see citing works

Digital Twins and Health Care: an Umbrella Review

Afshar, M., Moradkhani, A., Soheili, M., Tavakkol, M., Moradi, Y., & Kohan, H. G. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02322-9 · Cited by 6 · see citing works

Calibration of the Mechanical Boundary Conditions for a Patient-Specific Thoracic Aorta Model Including the Heart Motion Effect

Geronzi, L., Bel‐Brunon, A., Martínez, A. B., Rochette, M., Sensale, M., Bouchot, O., Lalande, A., Lin, S., Valentini, P. P., & Biancolini, M. E. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3287680 · Cited by 6 · see citing works

Cost-Effectiveness Analysis of Personalized Hypertension Prevention

Wang, S., Lin, T.-Y., Chen, T. H.-H., Chen, S. L., & Fann, J. C. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13061001 · Cited by 5 · see citing works

Common Information Model Extension for Multi-Energy Microgrids to Enable Digital Twin

He, X., Zhao, T., Cheng, S., & Gu, C. (2023) · International Conference on Energy, Electrical and Power Engineering (CEEPE) · doi:10.1109/ceepe58418.2023.10165910 · Cited by 5 · see citing works

Systems Anesthesiology

Cochand, L., Filipovic, M. G., Huber, M., Luedi, M. M., Urman, R. D., & Bello, C. (2023) · Anesthesiology Clinics · doi:10.1016/j.anclin.2023.05.006 · Cited by 5 · see citing works

Natural biopolymer/CuInS2 quantum dot-based red emissive, physically transient, and dynamically self-polarized piezoelectrets

Liu, Z., Lai, Y., Li, J., Xia, Z., Lu, L., Wang, C., Huang, B., Pan, C., Wen, J., Yang, W., et al. (2024) · Composites Communications · doi:10.1016/j.coco.2024.101919 · Cited by 5 · see citing works

Single‐cell digital twins identify drug targets and repurposable medicine in Alzheimer’s disease

Ren, Y., Hu, M., Li, Y. E., Pieper, A. A., Cummings, J. L., & Cheng, F. (2025) · Alzheimer s & Dementia · doi:10.1002/alz70859_102982 · Cited by 4 · see citing works

Configuration Testing of an Artificial Pancreas System Using a Digital Twin: An Evaluative Case Study

Somers, R. J., Walkinshaw, N., Hierons, R. M., Elliott, D., Iqbal, A., & Walkinshaw, E. (2025) · Software Testing Verification and Reliability · doi:10.1002/stvr.70000 · Cited by 8 · see citing works

Recent Advances and Future Directions in Alzheimer’s Disease Genetic Research

Stancheva, M., Toncheva, D. I., & Karachanak-Yankova, S. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26167819 · Cited by 4 · see citing works

Hyperreal Patients. Digital Twins as Simulacra and their impact on clinical heuristics

Rubeis, G. (2023) · Techno:Phil · doi:10.1007/978-3-662-65868-0_10 · Cited by 4 · see citing works

Stakeholder perspectives on contributors to delayed and inaccurate diagnosis of cardiovascular disease and their implications for digital health technologies: a UK-based qualitative study

Abdullayev, K., Gorvett, O., Sochiera, A., Laidlaw, L., Chico, T., Manktelow, M., Buckley, O. E., Condell, J. V., van Arkel, R. J., Díaz‐Zuccarini, V., et al. (2024) · BMJ Open · doi:10.1136/bmjopen-2023-080445 · Cited by 4 · see citing works

Towards the future of personalized medicine: digital twin technology

Mazuz, K., & Biswas, S. (2026) · Exploration of Digital Health Technologies · doi:10.37349/edht.2026.101181 · Cited by 3 · see citing works

Shaping the future of multiple myeloma with artificial intelligence and digital twins: from concept to clinic

Lee, C. H., Zhang, Y., McClure, B. J., Yong, A., Scott, H. S., & Kok, C. H. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1771531 · Cited by 3 · see citing works

PRISM: A Personalized, Rapid, and Immersive Skill Mastery Framework for Personalizing Experiential Learning Through Generative AI

Lin, Y.-Z., Patel, K., Alhamadah, A. H. J., Shih, B. P.-J., Redondo, M. W., Corona, D. R. V., Latibari, B. S., Pacheco, J., Salehi, S., & Satam, P. (2025) · Springer eBooks · doi:10.1007/978-3-032-01638-6_3 · Cited by 10 · see citing works

Physics-informed neuromorphic learning: Enabling scalable industrial digital twins

Li, X., Chen, C., Tang, Y., Xu, Q., Xu, L., Zhang, Y., Ding, Z., & Guan, X. (2025) · National Science Open · doi:10.1360/nso/20250016 · Cited by 2 · see citing works

GenDT-MAML: A Data-Efficient Framework for Personalized Seizure Prediction Using Generative Digital Twins and MAML

Li, M., Deng, Y., Huang, X., Li, J., & Xing, W. (2026) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2026.3658390 · Cited by 2 · see citing works

Mapping the landscape of AI-driven digital twins in medical diagnosis: A scoping review on core technologies, applications, and implementation barriers

Shen, S., Qi, W., Liu, X., Zeng, J., Li, S., Peng, G., Li, P., Wang, J., Lü, X., & Cao, S. (2026) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2026.103474 · Cited by 3 · see citing works

On the Use of Resilience Models as Digital Twins for Operational Support and In-time Decision Making

Irshad, L., & Hulse, D. E. (2023) · AIAA AVIATION Forum · doi:10.2514/6.2023-3559 · Cited by 2 · see citing works

Smart City Digital Twins: Overview of Implementation Challenges and Recommendations for Citizens Training

Njuguna, J., Sarwar, D., Laryea, E., & Far, A. H. (2023) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83753-022-920231007 · Cited by 2 · see citing works

A Novel Two-Layer DAG-Based Reactive Protocol for IoT Data Reliability in Metaverse

Yang, C., Liu, Y., Chin, K., Wang, J., Huang, H., & Zheng, Z. (2023) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs57875.2023.00049 · Cited by 2 · see citing works

Integration of Digital Twins and Artificial Intelligence for Classifying Cardiac Ischemia

Ammar, M., & Al-Raweshidy, H. S. (2023) · Journal on artificial intelligence · doi:10.32604/jai.2023.045199 · Cited by 2 · see citing works

Digital Twin Technology for Precision Medicine

Tripathi, S., & Chumnanvej, S. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-0659-9.ch006 · Cited by 2 · see citing works

Immune Digital Twin Blueprint: A Comprehensive Mechanistic Model of the Human Immune SystemPreprint

Amin, R., Aghamiri, S. S., Puniya, B. L., Mayo, L., Startsev, D., Poore, K., Helikar, R., & Helikar, T. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.11.988238 · Cited by 3 · see citing works

What’s new in critical illness and injury science? Keeping up with Moore’s law – Are we ready for the intensive care units of tomorrow?

Stawicki, K. B.-K., Galwankar, S., & Firstenberg, M. S. (2025) · International Journal of Critical Illness and Injury Science · doi:10.4103/ijciis.ijciis_27_25 · Cited by 1 · see citing works

Multi-task learning for multi-label electrocardiogram disease detection via point-level delineation-guided fusion

Wang, M., Tian, Y., Li, Z., Wei, X., Jin, Y., Liu, C., & Zhao, L. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111894 · Cited by 1 · see citing works

Cell-to-cell communication: from physical calling to remote emotional touching

Far, S. B., Qazani, M. R. C., & Rad, A. I. (2025) · Discover Nano · doi:10.1186/s11671-025-04363-y · Cited by 1 · see citing works

Development of the Windmill Model for Mapping Older Adults’ Intrinsic Capacity Using Digital Twin Technology: Descriptive Qualitative Study

Niu, Y., Xuan, Z., Wang, Y., Zhang, X., Guo, Y., Li, X., Jin, S., Zhao, J., Xiao, Q., & Chang, H. (2026) · JMIR Aging · doi:10.2196/81075 · Cited by 1 · see citing works

An edge–cloud continuum service for personalized seizure forecasting: communication-efficient digital twins with few-shot updates

Li, M., Zhang, W., Zhang, Y., Liu, Q., & Xing, W. (2026) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-026-00910-9 · Cited by 1 · see citing works

Artificial intelligence in drug development: From molecular discovery to precision medicine

Xie, T., Zhang, Z., Wang, Y., Wang, H., Chen, X., Feng, H., & Shao, F. (2026) · Genes & Diseases · doi:10.1016/j.gendis.2026.102402 · Cited by 1 · see citing works

COVIDMe: a digital twin for COVID-19 self-assessment and detection

Velazquez, R., Ceballos, F., Sanchez, A., Saddik, A. E., & Petriu, E. M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-32-399163-6.00012-3 · Cited by 1 · see citing works

HEURISTIC-ASSISTED ADAPTIVE HYBRID DEEP LEARNING MODEL WITH FEATURE SELECTION FOR EPILEPSY DETECTION USING EEG SIGNALS

Bhanja, N., Dhara, S. K., & Khampariya, P. (2023) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237223500369 · Cited by 1 · see citing works

Applying a Digital Twin Approach for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Sfera, A., Nanyonga, S., & Kozlakidis, Z. (2023) · Innovations in Digital Health Diagnostics and Biomarkers · doi:10.36401/iddb-23-4 · Cited by 1 · see citing works

[Digital twin hospitals: transforming the future of healthcare].

Hu, H., Wang, M., Lei, Q., Yang, K., Sun, H., Liu, X., & Wu, S. (2024) · PubMed Central · doi:10.7507/1001-5515.202310041 · Cited by 1 · see citing works

Uncovering personalised glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modellingPreprint

Phillips, N. E., Collet, T., & Naef, F. (2022) · medRxiv · doi:10.1101/2022.08.20.22278813 · Cited by 1 · see citing works

Hypertension and cardiac fatigue

Choudhury, A. D., Banerjee, R., Kimbahune, S., & Pal, A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824499-9.00006-4

Complex data representation, modeling and computational power for a personalized dialysis

Hueso, M., Valencia, A., Carbonell‐Caballero, J., Montagud, A., Álvarez, R., & Vellido, A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-18498-7.00006-5

Stakeholder perspectives on contributors to delayed and inaccurate diagnosis of cardiovascular disease: a UK-based qualitative studyPreprint

Abdullayev, K., Gorvett, O., Sochiera, A., Laidlaw, L., Chico, T., Manktelow, M., Buckley, O. E., Condell, J. V., Arkel, R. V., Díaz‐Zuccarini, V., et al. (2023) · medRxiv · doi:10.1101/2023.09.28.23295847

Digital Twins Prevalence in the Medical Fields: A Bibliomatrics Study and Visualization Analysis via CiteSpace (Preprint)Preprint

Zhou, T. T. (2023) · doi:10.2196/preprints.54522

Biopharma 4.0 for Biologics Manufacturing Under Pandemic Constraints

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