ArticlePNAS nexus2025
The future of in silico trials and digital twins in medicine.
Article in PNAS nexus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Big Data and Artificial Intelligence in Cancer Drug Discovery: Promise, Challenges, and Emerging Opportunities.Cancers · 2026Review
- Complementary roles of virtual and real trials in medicine: An interplay of virtuous spirals.PNAS nexus · 2026Article
- Precision oncology in the age of AI: lessons from AI-driven drug discovery and clinical translation.BJC reports · 2026Review
- Impact of OMICS Technologies in Our Understanding of the Pathogenesis of Peri-Implantitis.Clinical and experimental dental research · 2026Review
- Development of a vasopressor control module for testing hemorrhagic shock resuscitation controllers.Biomedical engineering online · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Evaluation of Cerebral Blood Flow and Cerebral Autoregulation Using Synthetic Data and In Silico Modeling.CNS neuroscience & therapeutics · 2026Article
- Article
- Technological innovation in healthcare: challenges, opportunities and impact on sustainability.Frontiers in digital health · 2026Review
- Role of health digital twins in oncology drug development - a primer.Frontiers in oncology · 2026Review
- A survey on ethical digital twins in healthcare.Frontiers in artificial intelligence · 2026Review
- Engineering design of platelet-mimicking therapeutic systems: multilevel biomimicry, gating strategies, and translational boundaries.Frontiers in bioengineering and biotechnology · 2026Review
- AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2025Review
- Digital twins: pioneering personalized precision in modern surgery.Annals of medicine and surgery (2012) · 2025Article
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
In silico trials and digital twins are emerging as transformative medical technologies, as they offer a unique way to design medical innovations, optimize their application, and evaluate their utility. Their use spans from individual care-appropriating the technology for personalized decision, to population care-presenting an alternative to design, supplement, or replace clinical trials. They effectually offer a new way to efficiently qualify, quantify, and personalize healthcare innovations in advance or in conjunction with clinical application. While much progress is underway to advance these technologies across diverse developments, realizing their full potential requires a cohesive goal to unify separate activities towards a common objective. Such a cohesive goal-moonshot-can be defined as forming and fostering a digital twin of every single human person, owned by the individual, progressively updated with new data, and used to deliver optimized care, technology assessment, and real-world evidence. The feasibility of such a vision builds upon a growing body of work in computational modeling, regulatory science, and digital healthcare. Bringing this vision to reality requires ownership and active engagement of all stakeholders to contribute diverse expertise and resources for transforming medicine and medical appropriation towards a more accurate, efficient, and quantitative future.
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.