ArticleDigital health
A comprehensive review of digital twin in healthcare in the scope of simulative health-monitoring.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of AI-assisted drug repurposing in neurological disorders: a systematic review of validation strategies, challenges and opportunities.Journal of nanobiotechnology · 2026Pooled it
- Beyond Traditional Risk Scores: Artificial Intelligence in Coronary Plaque Characterization and Personalized Atherosclerosis Management.Journal of cardiovascular development and disease · 2026Review
- Pulmonary Drug Delivery in the Era of Nanomedicine: From Biological Barriers to Artificial Intelligence-Driven Optimization.Pharmaceuticals (Basel, Switzerland) · 2026Review
- A One Health digital twin framework for leprosy: linking host, pathogen, and population dynamics.Wiener medizinische Wochenschrift (1946) · 2026Article
- Review
- Article
- Enhancing the resilience of remote patient monitoring and hospital-at-home systems: a digital-twin-based framework.npj health systems · 2026Review
- A decade of innovation in healthcare: Automation, bio-printing and digital twin technologies for personalized therapies.International journal of pharmaceutics: X · 2026Review
- Digital Twins in Orthopedics and Trauma: Concepts, Emerging Evidence, and Barriers to Clinical Translation.Journal of clinical medicine · 2026Review
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- Digital twins and digital models of the human circulatory system.Nature reviews bioengineering · 2026Article
- From genes to germ layers: virtual twins of gastruloids.NPJ systems biology and applications · 2026Review
- A unified digital twin framework for predicting therapeutic response to central nervous system infections by pathogenic free-living amoebae.Parasitology research · 2026Review
- Current Approaches to the Management of Acute Surgical and Medical Emergencies: A Structured Narrative Review.Cureus · 2026Review
- Embracing the Digital Revolution: How Artificial Intelligence is Transforming Clinical Trials in Older Participants.Drugs & aging · 2026Review
- A digital twin forJournal of medical microbiology · 2026Article
- A cost-optimized medical digital twin framework for secure and efficient patient data management in smart healthcare.Scientific reports · 2026Article
- Digital twin frameworks for polio and post-polio neurodegeneration: Toward predictive, personalised lifelong neuro-rehabilitation.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Review
- Active Rehabilitation Technologies for Post-Stroke Patients.Biosensors · 2025Review
- The role of digital twins in P4 medicine: A paradigm for modern healthcare.NPJ digital medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Digital twins (DTs) emerged in the wake of Industry 4.0 and the creation of cyber-physical systems, motivated by the increased availability and variability of machine and sensor data. DTs are a concept to create a digital representation of a physical entity and imitate its behavior, while feeding real-world data to the digital counterpart, thus allowing enabling digital simulations related to the real-world entity. The availability of new data sources raises the potential for developing structured approaches for prediction and analysis. Similarly, in the field of medicine and digital healthcare, the collection of patient-focused data is rising. Medical DTs, a new concept of structured, exchangeable representations of knowledge, are increasingly used for capturing personal health, targeting specific illnesses, or addressing complex healthcare scenarios in hospitals. Methods: This article surveys the current state-of-the-art in applying DTs in healthcare, and how these twins are generated to support smart, personalized medicine. These concepts are applied to a DT for a simulated health-monitoring scenario. Results: The DT use case is implemented using AnyLogic multi-agent simulation, monitoring the patient's personal health indicators and their development. Conclusion: The results indicate both possibilities and challenges and provide important insights for future DT implementations in healthcare. They have the potential to optimize healthcare in various ways, such as providing patient-centered health-monitoring.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.