ArticleFrontiers in digital health2022
The silent trial - the bridge between bench-to-bedside clinical AI applications.
Article in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
34 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- AI-assisted ultrasound for trauma: a scoping review.BMC medical imaging · 2026Pooled it
- Systematic review and meta-analysis of machine learning models predicting massive hemorrhage protocol in trauma.Scientific reports · 2026Pooled it
- AI implementation in pediatric radiology for patient safety: a multi-society statement from the ACR, ESPR, SPR, SLARP, AOSPR, SPIN.Pediatric radiology · 2026Guideline
- Agentic AI workflows in anatomic pathology: Opportunities and challenges.Journal of pathology informatics · 2026Review
- AI-based augmentation of oncology clinical trials.Nature reviews. Clinical oncology · 2026Review
- A five-phase evaluation framework for diagnostic and predictive medical artificial intelligence.NPJ digital medicine · 2026Article
- Reporting of race and ethnicity in studies of artificial intelligence in pediatric urology A secondary analysis of the AI-PEDURO online repository.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2026Review
- Clinical AI Beyond Development: A Scoping Review of Deployment-Related Robustness, Algorithmovigilance, and Lifecycle Oversight.Healthcare (Basel, Switzerland) · 2026Review
- Digital Twins as the Implementation Layer of Precision Medicine in Pediatric Neurosurgery.Journal of Korean Neurosurgical Society · 2026Review
- Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature.Diagnostics (Basel, Switzerland) · 2026Review
- Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology.Journal of imaging informatics in medicine · 2026Review
- Clinically-applicable prediction of hospital stay and patient similarity retrieval in paediatric cardiology using machine learning.Nature communications · 2026Article
- Barriers and opportunities of scaling ambient AI scribes for clinical documentation across diverse healthcare settings.NPJ digital medicine · 2026Review
- In vitro to in vivo translation of artificial intelligence for clinical use: screening for acute coronary syndrome to identify ST-elevation myocardial infarction.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Using generative artificial intelligence in clinical practice: a narrative review and proposed agenda for implementation.The Medical journal of Australia · 2025Review
- AI for dental imaging: impressive in vitro, but what about in practice?Evidence-based dentistry · 2025Article
- Development and prospective evaluation of a machine learning model to predict vomiting among pediatric cancer and hematopoietic cell transplant patients.BMC cancer · 2025Article
- Leveraging artificial intelligence for the management of preschool wheeze: A narrative review.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2025Review
- Unreasonable effectiveness of training AI models locally.BMJ quality & safety · 2025Article
- Longitudinal image-based prediction of surgical intervention in infants with hydronephrosis using deep learning: Is a single ultrasound enough?PLOS digital health · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As more artificial intelligence (AI) applications are integrated into healthcare, there is an urgent need for standardization and quality-control measures to ensure a safe and successful transition of these novel tools into clinical practice. We describe the role of the silent trial, which evaluates an AI model on prospective patients in real-time, while the end-users (i.e., clinicians) are blinded to predictions such that they do not influence clinical decision-making. We present our experience in evaluating a previously developed AI model to predict obstructive hydronephrosis in infants using the silent trial. Although the initial model performed poorly on the silent trial dataset (AUC 0.90 to 0.50), the model was refined by exploring issues related to dataset drift, bias, feasibility, and stakeholder attitudes. Specifically, we found a shift in distribution of age, laterality of obstructed kidneys, and change in imaging format. After correction of these issues, model performance improved and remained robust across two independent silent trial datasets (AUC 0.85-0.91). Furthermore, a gap in patient knowledge on how the AI model would be used to augment their care was identified. These concerns helped inform the patient-centered design for the user-interface of the final AI model. Overall, the silent trial serves as an essential bridge between initial model development and clinical trials assessment to evaluate the safety, reliability, and feasibility of the AI model in a minimal risk environment. Future clinical AI applications should make efforts to incorporate this important step prior to embarking on a full-scale clinical trial.
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