ReviewFrontiers in digital health2023
Digital twin for healthcare systems.
Review in Frontiers in digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 2 of them syntheses 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
92 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Digital Twins and Health Care: an Umbrella Review.Journal of medical systems · 2025Pooled it
- Advancing Health Care With Digital Twins: Meta-Review of Applications and Implementation Challenges.Journal of medical Internet research · 2025Pooled it
- A data-driven digital twin of emergency department flow to quantify congestion dynamics and test operational resilience.Internal and emergency medicine · 2026Article
- Digital Twins for Hospital and Healthcare Operations: A Systematic Review of Resource Allocation, Infection Control, and Workflow Optimization.Bioengineering (Basel, Switzerland) · 2026Review
- Digital Twin-Based Virtual Hospital Platform for IT Outage Disaster Response Training: Implementation and Evaluation Study.JMIR formative research · 2026Article
- A Lean-based Digital Twin model for planning and governance in secondary and tertiary hospital networks.BMC health services research · 2026Article
- Healthcare Digital Twins Across Scales: A Narrative Review and Five-Level Conceptual Framework.Healthcare (Basel, Switzerland) · 2026Review
- AI-based augmentation of oncology clinical trials.Nature reviews. Clinical oncology · 2026Review
- Leveraging Advanced AI Frameworks for Dual PPAR α/γ Agonist Discovery in Alzheimer's Disease.ACS chemical neuroscience · 2026Review
- Validating medical digital twins for clinical decision support: beyond predictive accuracy.JAMIA open · 2026Review
- AI-Driven Digital Twin Architecture for Multimodal Prediction and Adaptive Intervention in Cognitive Aging.JMIR AI · 2026Article
- A Survey on Security Threats and Mitigation Mechanisms for Smart Hospitals in the 6G Era.Sensors (Basel, Switzerland) · 2026Review
- AI in UK Medical Education: A Framework for Curriculum Reform.JMIR medical education · 2026Article
- Digital twins to accelerate target identification and drug development for immune-mediated disorders.FEBS open bio · 2026Review
- Combining Subjective Perceptions and Objective Behavioral Metrics With the Elderly Digital Twin System: Quantitative Usability Study.JMIR aging · 2026Article
- Optimized explainable AI and digital twin for patient flow improvement in ICU during respiratory epidemics.BMC medical informatics and decision making · 2026Article
- The Role of AI in Clinical Trial Design and Scientific Writing.Cardiovascular and interventional radiology · 2026Review
- Digital Twin Technology In Radiology.Journal of imaging informatics in medicine · 2026Review
- From Knowledge Graphs to Digital Twins: Perspectives on Modeling Patient Outcomes for Health Care Quality Assessment.Journal of medical Internet research · 2026Article
- Who's afraid of synthetic data? Hybrid approaches to deliver medical digital twins.Informatics in medicine unlocked · 2026Article
32 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital twin technology is revolutionizing healthcare systems by leveraging real-time data integration, advanced analytics, and virtual simulations to enhance patient care, enable predictive analytics, optimize clinical operations, and facilitate training and simulation. With the ability to gather and analyze a wealth of patient data from various sources, digital twins can offer personalized treatment plans based on individual characteristics, medical history, and real-time physiological data. Predictive analytics and preventive interventions are made possible by machine learning algorithms, allowing for early detection of health risks and proactive interventions. Digital twins can optimize clinical operations by analyzing workflows and resource allocation, leading to streamlined processes and improved patient care. Moreover, digital twins can provide a safe and realistic environment for healthcare professionals to enhance their skills and practice complex procedures. The implementation of digital twin technology in healthcare has the potential to significantly improve patient outcomes, enhance patient safety, and drive innovation in the healthcare industry.
Indexed as
Identifiers
What OpenQuestion holds
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.