Digital Twins and AI-Enabled Trial Architectures: A Narrative Review
DOI:
https://doi.org/10.65761/pjcr.2026.35Keywords:
Computer Simulation, Artificial Intelligence, Clinical Trials as Topic, Precision Medicine, Decentralized Clinical Trials, Data Governance.Abstract
Background: Traditional population-averaged randomized controlled trials are constrained by severe recruitment bottlenecks, skyrocketing structural development costs, and poor real-world generalizability. Digital Twins in Medicine (DTM) have emerged as a high-fidelity, paradigm-shifting architectural ecosystem capable of addressing these operational vulnerabilities.
Objective: This narrative review systematically synthesizes the structural frameworks, cross-domain clinical efficacy profiles, and current regulatory and computational bottlenecks of AI-enabled digital twins in next-generation clinical trials.
Methods: Following SANRA guidelines, a systematic literature search was executed across PubMed/MEDLINE, Scopus, and Web of Science for studies published from January 2015 to June 2025. Applying explicit eligibility criteria, 42 high-fidelity peer-reviewed investigations were selected for qualitative thematic synthesis.
Results: The synthesized data revealed a consistent three-layer architectural platform: data acquisition, computational modeling, and clinical actionability. In clinical deployment, DTM achieved a 34% reduction in Phase II oncology cohort sizes via Synthetic Control Arms, an Area Under the Receiver Operating Characteristic (AUROC) curve greater than 0.92 for predicting structural cardiac remodeling, and a 4.2-week lead-time advantage in mapping chronic immunological flares via wearable biometric telemetry. However, systemic methodological vulnerabilities persist, including a prominent black-box interpretability gap across 78% of reviewed models, baseline covariate shift (42%), and data attrition or telemetry noise (31%).
Conclusion: AI-enabled digital twins are actively transitioning from speculative conceptual models into clinically actionable, practice-changing investigative platforms. Securing extensive regulatory endorsement and achieving scaling milestones will require standardized Explainable AI (XAI) frameworks, multicenter geographic validation loops, and robust data-quality governance structures.
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Copyright (c) 2026 Ambar Fatima, Shiza Amir, Sadia Salim, Aqila Azam
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