ReviewThe Lancet. Digital health2025
Medical digital twins: enabling precision medicine and medical artificial intelligence.
Review in The Lancet. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 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
78 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Perception, assessment, and coaching: a systematic review and taxonomy of computer vision-based physical rehabilitation techniques.Frontiers in rehabilitation sciences · 2026Pooled it
- An explainable online frailty prediction model for community-dwelling older adults based on machine learning algorithms: a cross-sectional study based on retrospective health data.Annals of medicine · 2026Article
- Converging engineered models of cancer and artificial intelligence.Nature reviews. Cancer · 2026Article
- Heterogeneity in cancer: molecular mechanisms and therapeutic strategies.Signal transduction and targeted therapy · 2026Review
- The Next Frontier in Quantitative Co-Clinical Imaging to Advance Functional Precision Oncology.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Decoupling Structural and Vascular Biomarkers in Contrast-Enhanced Mammography: A Methodological Synthesis and Perspective.Diagnostics (Basel, Switzerland) · 2026Article
- Integrating ex vivo platforms with AI to guide glioblastoma treatment.Journal of neuro-oncology · 2026Review
- Structural requirements for intelligent clinical digital twins in feedback-driven care.npj health systems · 2026Review
- AI-based augmentation of oncology clinical trials.Nature reviews. Clinical oncology · 2026Review
- From intravascular imaging to adaptive vascular care: intelligent photonics and digital twins in panvascular disease.Light, science & applications · 2026Review
- Perceptions of Artificial Intelligence in Gynecologic Oncology Education: Insights from a Qualitative Study Using Semi-Structured Interviews.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026Article
- Informing agent-based models with spatial data using convolutional autoencoders.Bioinformatics (Oxford, England) · 2026Article
- Causal graph neural networks for healthcare.Nature biomedical engineering · 2026Review
- From pixels to precision: a narrative review of AI-driven 3D morphological analysis and digital twinning in alveolar cleft management.Translational pediatrics · 2026Review
- The Impact of Nutrition on DNA Methylation: Methodological Challenges in Understanding Cause and Effect.Nutrients · 2026Review
- Multifunctional Nano-Contrast Agent Carriers: From Traditional Platforms to Next-Generation Theranostic Applications in Molecular Imaging.Biomedicines · 2026Review
- Are Traditional Registries Becoming Obsolete in the Modern Digital Health Ecosystem?Journal of medical Internet research · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Adaptive resistance in cancer immunotherapy.Cellular & molecular immunology · 2026Review
- Digital Twins as the Implementation Layer of Precision Medicine in Pediatric Neurosurgery.Journal of Korean Neurosurgical Society · 2026Review
18 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.
Funding
Abstract
The notion of medical digital twins is gaining popularity both within the scientific community and among the general public; however, much of the recent enthusiasm has occurred in the absence of a consensus on their fundamental make-up. Digital twins originate in the field of engineering, in which a constantly updating virtual copy enables analysis, simulation, and prediction of a real-world object or process. In this Health Policy paper, we evaluate this concept in the context of medicine and outline five key components of the medical digital twin: the patient, data connection, patient-in-silico, interface, and twin synchronisation. We consider how various enabling technologies in multimodal data, artificial intelligence, and mechanistic modelling will pave the way for clinical adoption and provide examples pertaining to oncology and diabetes. We highlight the role of data fusion and the potential of merging artificial intelligence and mechanistic modelling to address the limitations of either the AI or the mechanistic modelling approach used independently. In particular, we highlight how the digital twin concept can support the performance of large language models applied in medicine and its potential to address health-care challenges. We believe that this Health Policy paper will help to guide scientists, clinicians, and policy makers in creating medical digital twins in the future and translating this promising new paradigm from theory into clinical practice.
Indexed as
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.