Digital Twin Technology in Healthcare: Computational Modeling, AI-Driven Clinical Applications, and Personalized Medicine
A Special Issue of Bioengineering (ISSN 2306-5354) belonging to the section "Biosignal Processing".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 734
Editors
Interests: medical imaging; computer vision; pattern recognition; big data; machine and deep learning; computer-aided diagnostic systems; artificial intelligence in medicine
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence (AI); machine learning; deep learning; robotics; metaheuristics; computer-assisted diagnosis systems; computer vision; bioinspired optimization algorithms; smart systems engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of Digital Twin (DT) technology into healthcare represents one of the most consequential technological shifts in the 21st century. A digital twin in healthcare is a dynamic, patient-specific virtual model—continuously updated with real-time physiological, genomic, and clinical data—that enables clinicians and researchers to simulate disease progression, test interventions, and optimize treatment pathways without risk to the patient. This paradigm is fundamentally reshaping how medicine is practiced, from personalized drug dosing and surgical planning to hospital operations management and pandemic response modeling.
This proposed Special Issue invites original research and comprehensive review articles at the intersection of digital twin frameworks, computational biomedical modeling, and Artificial Intelligence (AI) in healthcare. Contributions are sought to address the full pipeline, from patient-specific model construction and multi-scale physiological simulation to AI-powered clinical decision support, real-time monitoring, and regulatory-compliant deployment in clinical settings. Special emphasis is placed on novel computational methods—including physics-informed machine learning, finite element biomodelling, and data-driven surrogate models—that advance the field beyond the proof of concept toward clinical translation.
By uniting computational engineers, biomedical scientists, clinical researchers, and AI practitioners under CMES's established computational modeling umbrella, this Special Issue will produce a landmark collection that accelerates the translation of digital twin technology from research laboratories into hospitals, rehabilitation centers, and personalized care ecosystems worldwide.
- Aims and Scope
Healthcare systems globally face mounting pressures: aging populations, the rise in chronic and complex diseases, escalating costs, and the demand for personalized rather than population-average medicine. Digital twin technology, powered by AI and advanced computational modeling, offers a transformative response—enabling virtual patient models that evolve with real-world data to support diagnosis, prognosis, treatment planning, and healthcare delivery optimization.
This Special Issue aims to consolidate the rapidly growing body of knowledge on computational digital twin methods in healthcare, providing an authoritative platform for researchers advancing the field across engineering, medicine, and computer science. We welcome contributions spanning the following topics:
- Patient-specific digital twin construction: multi-modal data integration (imaging, genomics, wearables, and EHR).
- Computational cardiovascular modeling: hemodynamics simulation, cardiac digital twins, and personalized treatment planning.
- Musculoskeletal and orthopedic digital twins: finite element modeling for implant design, rehabilitation, and surgical planning.
- Neurological digital twins: brain network modeling, epilepsy prediction, and neurodegenerative disease simulation.
- Oncology applications: tumor growth modeling, radiation therapy optimization, and drug delivery simulation.
- Physics-informed neural networks (PINNs) and data-driven surrogate models for biological system simulation.
- AI and machine learning for real-time patient monitoring, anomaly detection, and early warning systems.
- Digital twins for hospital and healthcare operations: resource allocation, infection control, and workflow optimization.
- Wearable IoT devices and sensor fusion for continuous physiological digital twin synchronization.
- Federated learning and privacy-preserving AI for multi-institutional digital twin training.
- Regulatory frameworks, validation methodologies, and clinical translation of digital twin models.
- Digital twins for drug discovery, pharmacokinetic modeling, and precision dosing.
- Rehabilitation robotics and assistive technology guided by patient digital twin feedback.
- Pandemic and epidemiological modeling using population-level digital twin frameworks.
- Ethical, explainability, and trustworthiness considerations in clinical AI and digital twin deployment.
Dr. Mohamed Shehata
Prof. Dr. Mostafa Elhosseini
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- digital twin
- healthcare AI
- patient-specific modeling
- computational biomechanics
- personalized medicine
- cardiovascular simulation
- physics-informed neural networks
- clinical decision support
- wearable IoT
- federated learning
- finite element biomodelling
- drug delivery simulation
- surgical planning
- real-time patient monitoring
- oncology modeling
- rehabilitation robotics
- epidemiological simulation
- precision medicine
- medical image analysis
- trustworthy AI in healthcare
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