ArticleClinical and translational science2024
Increasing acceptance of AI-generated digital twins through clinical trial applications.
Article in Clinical and translational science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Regulatory Adoption of AI, ML, Computational Modeling & Simulation in In-Silico Clinical Trials for Medical Devices: A Systematic Review.Therapeutic innovation & regulatory science · 2026Pooled 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
- Causal inference and digital twins: a roadmap for the future of clinical trials.NPJ digital medicine · 2026Review
- A digital twin methodology using retrospective patient data for sample size reduction in Alzheimer's disease clinical trials.Alzheimer's research & therapy · 2026Article
- Digital Twin Technology In Radiology.Journal of imaging informatics in medicine · 2026Review
- Embracing the Digital Revolution: How Artificial Intelligence is Transforming Clinical Trials in Older Participants.Drugs & aging · 2026Review
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- International Delphi consensus on acute kidney injury: Foundations for AI-driven digital twin development in critical care nephrology.PloS one · 2026Article
- The expanding role of artificial intelligence in personalised medicine: from innovation to individualized care.Frontiers in medicine · 2026Review
- Digital twins for personalized treatment in uro-oncology in the era of artificial intelligence.Nature reviews. Urology · 2026Review
- Healthcare professionals' perspectives on usefulness, acceptability and implementation conditions of socially assistive robots in France: a cross-sectional survey and cluster analysis.Frontiers in digital health · 2026Article
- Active Rehabilitation Technologies for Post-Stroke Patients.Biosensors · 2025Review
- Mining the gaps: Deciphering Alzheimer's biology through AI-driven reconciliation.The journal of prevention of Alzheimer's disease · 2025Article
- Leveraging artificial intelligence in antibody-drug conjugate development: from target identification to clinical translation in oncology.NPJ precision oncology · 2025Review
- AI and innovation in clinical trials.NPJ digital medicine · 2025Article
- Artificial Intelligence in Clinical and Translational Science: From Bench Insights to Bedside Impact.Clinical and translational science · 2025Article
- A digital twin methodology using real patient data for sample size reduction in Alzheimer's disease randomized controlled clinical trials.medRxiv : the preprint server for health sciences · 2025Article
- Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities.Journal of clinical medicine · 2025Article
- Enhancing randomized clinical trials with digital twins.NPJ systems biology and applications · 2025Review
- Ophthalmic drug discovery and development using artificial intelligence and digital health technologies.NPJ digital medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Today's approach to medicine requires extensive trial and error to determine the proper treatment path for each patient. While many fields have benefited from technological breakthroughs in computer science, such as artificial intelligence (AI), the task of developing effective treatments is actually getting slower and more costly. With the increased availability of rich historical datasets from previous clinical trials and real-world data sources, one can leverage AI models to create holistic forecasts of future health outcomes for an individual patient in the form of an AI-generated digital twin. This could support the rapid evaluation of intervention strategies in silico and could eventually be implemented in clinical practice to make personalized medicine a reality. In this work, we focus on uses for AI-generated digital twins of clinical trial participants and contend that the regulatory outlook for this technology within drug development makes it an ideal setting for the safe application of AI-generated digital twins in healthcare. With continued research and growing regulatory acceptance, this path will serve to increase trust in this technology and provide momentum for the widespread adoption of AI-generated digital twins in clinical practice.
Indexed as
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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.