ArticleCancers2024
Advancing Precision Oncology with Digital and Virtual Twins: A Scoping Review.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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
26 citing papers in PubMed.
- Digital solutions, real-world challenges: lessons from mHealth trials in oncology.Frontiers in digital health · 2025Trial
- Digital Twins for Targeted Therapy in Head and Neck Cancer: From Molecular Stratification to Resistance-Aware Combination Strategies.Current oncology (Toronto, Ont.) · 2026Review
- When Cancer Research Met Artificial Intelligence: From Machine Learning to Intelligent Oncology.Cancers · 2026Article
- AI-Enabled Real-World Evidence in Oncology: A Statistical Perspective for Regulatory Decisions.Therapeutic innovation & regulatory science · 2026Review
- Review
- Healthcare Digital Twins Across Scales: A Narrative Review and Five-Level Conceptual Framework.Healthcare (Basel, Switzerland) · 2026Review
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Translational barriers to digital twins in radiation oncology.Physics and imaging in radiation oncology · 2026Article
- Enhancing Targeted Colorectal Cancer Therapies with Natural Products: Mechanistic Pathways.Biomedicines · 2026Review
- Exploring the scope and applications of digital twin technologies in dentistry: a scoping review.Evidence-based dentistry · 2026Article
- Comparative Molecular Insights and Computational Modeling of Multiple Myeloma and Osteosarcoma.International journal of molecular sciences · 2026Review
- Who's afraid of synthetic data? Hybrid approaches to deliver medical digital twins.Informatics in medicine unlocked · 2026Article
- Deep learning in lower gastrointestinal cancer detection: Advances in endoscopic, radiologic, and histopathologic diagnostics.World journal of gastrointestinal oncology · 2026Review
- Clinical Outcomes of Hearing Aid Use in Moderate to Severe Sensorineural Hearing Loss: A Cross-Sectional Study from Romania.Healthcare (Basel, Switzerland) · 2026Article
- Shaping the future of multiple myeloma with artificial intelligence and digital twins: from concept to clinic.Frontiers in digital health · 2026Review
- Beyond static biomarkers: systems biology and AI for decoding cancer dynamics.Frontiers in systems biology · 2026Review
- Digital twin-supported behavioral intention in mothers of young children to prevent childhood obesity: a large language model-based intervention study.Frontiers in artificial intelligence · 2026Article
- Nanotechnology-Driven Cancer Therapies for Precision Oncology: Advances and Clinical Outlook.International journal of nanomedicine · 2026Review
- From images to physics-based computational models to digital twins: a framework for personalized cancer therapies.Frontiers in radiology · 2026Article
- The Heterogeneity and Function of Stromal Cells in the Tumor Microenvironment.Research (Washington, D.C.) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital twins (DTHs) and virtual twins (VTHs) in healthcare represent emerging technologies towards precision medicine, providing opportunities for patient-centric healthcare. Our scoping review aimed to map the current DTH and VTH technologies in oncology, summarize their technical solutions, and assess their credibility. A systematic search was conducted in the main bibliographic databases, identifying 441 records, of which 30 were included. The studies covered a wide range of cancers, including breast, lung, colorectal, and gastrointestinal malignancies, with DTH and VTH applications focusing on diagnosis, therapy, and monitoring. The results revealed heterogeneity in targeted topics, technical approaches, and outcomes. Most twining solutions use synthetic or limited real-world data, raising concerns regarding their reliability. Few studies have integrated real-time data and machine learning for predictive modeling. Technical challenges include data integration, scalability, and ethical considerations, such as data privacy and security. Moreover, the evidence lacks sufficient clinical validation, with only partial credibility in most cases. Our findings underscore the need for multidisciplinary collaboration among end-users and developers to address the technical and ethical challenges of DTH and VTH systems. Although promising for the future of personalized oncology, substantial steps are required to move beyond experimental frameworks and to achieve clinical implementation.
Indexed as
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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.