ArticleNPJ digital medicine2024
Digital twins for health: a scoping review.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 298 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
298 citing papers in PubMed, 2 syntheses or guidelines pooled it, 472 citations in OpenAlex.
- Digital Twins in Neuro-Oncology: A Systematic Review of Current Implementations, Technical Strategies, and Clinical Applications.Radiology. Imaging cancer · 2026Pooled it
- AI-driven techniques for detection and mitigation of SARS-CoV-2 spread: a review, taxonomy, and trends.Clinical and experimental medicine · 2025Pooled it
- Synthetic control methods for n-of-1 and parallel-group trials in Alzheimer's disease: A proof-of-concept study using the I-CONECT.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Trial
- Biophotonics point-of-care diagnostics in low-resource settings: a South African perspective.Journal of biomedical optics · 2026Review
- A digital twin for tracking and forecasting glycemia with septic patients in ICUs.npj metabolic health and disease · 2026Article
- A data-driven digital twin of emergency department flow to quantify congestion dynamics and test operational resilience.Internal and emergency medicine · 2026Article
- Beyond robotic platforms: artificial intelligence and the emergence of intelligent surgical ecosystems in colorectal surgery.Journal of robotic surgery · 2026Review
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Harnessing digital twins for public health surveillance and pandemic preparedness.BMC proceedings · 2026Article
- Digital Twins for Targeted Therapy in Head and Neck Cancer: From Molecular Stratification to Resistance-Aware Combination Strategies.Current oncology (Toronto, Ont.) · 2026Review
- Beyond Traditional Risk Scores: Artificial Intelligence in Coronary Plaque Characterization and Personalized Atherosclerosis Management.Journal of cardiovascular development and disease · 2026Review
- A global digital navigator of human health for precision medicine.Nature medicine · 2026Article
- New approach methodologies (NAMs) for preclinical and translational evaluation of mRNA-lipid nanoparticle (LNP) therapeutics.Journal of controlled release : official journal of the Controlled Release Society · 2026Review
- When Cancer Research Met Artificial Intelligence: From Machine Learning to Intelligent Oncology.Cancers · 2026Article
- The Role of Metals and Trace Elements in the Pathogenesis of Osteoarthritis and Other Rheumatic Diseases.International journal of molecular sciences · 2026Review
- Structural requirements for intelligent clinical digital twins in feedback-driven care.npj health systems · 2026Review
- Exercise-induced neuropeptidergic and neurochemical neuroadaptation in stress regulation and emotional disorders.Acta neurologica Belgica · 2026Review
- The physiology of survival: Space.Experimental physiology · 2026Article
- Digital twin for neurological conditions: a systematic scoping review.Biomedical engineering letters · 2026Review
- Integration of biological avatars and digital twins for "ex vivo clinical trials".EBioMedicine · 2026Review
238 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
13 authors at 12 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology.
Identifiers
What Socratic 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 Socratic graph.