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Cited by 17Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareExplainable Artificial Intelligence (XAI)

A scoping review of silent trials for medical artificial intelligence

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Tikhomirov, Lana, Semmler, Carolyn, Prizant, Noah, Bhasin, Srijan, Kenyon, Georgia, van der Vegt, Anton, Erdman, Lauren, Kurian, Nikhil Cherian +19 more·2026·Nature Health
DOI 10.1038/s44360-025-00048-z
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Abstract

A ‘silent trial’ refers to the prospective, noninterventional testing of artificial intelligence (AI) models in the intended clinical setting without affecting patient care or institutional operations. The silent evaluation phase has received less attention than in silico algorithm development or formal clinical evaluations, despite its increasing recognition as a critical phase. There are no formal guidelines for performing silent AI evaluations in healthcare settings. We conducted a scoping review to identify silent AI evaluations described in the literature and to summarize current practices for performing silent testing. We screened the PubMed, Web of Science and Scopus databases for articles fitting our criteria for silent AI evaluations, or silent trials, published from 2015 to 2025. A total of 891 articles were identified, of which 75 met the criteria for inclusion in the final review. We found wide variance in terminology, description and rationale for silent evaluations, leading to substantial heterogeneity in the reported information. Overwhelmingly, the papers reported measurements of area under the curve and similar metrics of technical performance. Far fewer studies reported verification of outputs against an in situ clinical ground truth; when reported, the approaches varied in comprehensiveness. We noted less discussion of sociotechnical components, such as stakeholder engagement and human–computer interaction elements. We conclude that there is an opportunity to bring together diverse evaluative practices (for example, from data science, human factors and other fields) if the silent evaluation phase is to be maximally effective. These gaps mirror challenges in the effective translation of AI tools from computer to bedside and identify opportunities to improve silent evaluation protocols that address key needs.

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  • No previous review has integrated review-level and review-oriented evidence across post-development robustness, deployment-proximal evaluation, post-deployment monitoring, adaptive updating, and lifecycle governance in clinical AI practice.

    “to our knowledge, no previous review has integrated review-level and review-oriented evidence across post-development robustness, deployment-proximal evaluation, post-deployment monitoring, adaptive updating, and lifecycle governance in clinical AI practice [18,19,20].”

    From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [20]

    The Abstract and Discussion (page unavailable) describe a scoping review of silent trials: evaluations after model validation but before clinical use. The Model evaluation during the silent phase section also discusses performance shifts and occasional model updating. The paper does not establish that no previous review integrated all the listed topics, and its stated scope does not extend to post-deployment monitoring or lifecycle governance. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • These reviews differ substantially in design, scope, evidential intent, and proximity to live clinical use.

    “but these reviews differ substantially in design, scope, evidential intent, and proximity to live clinical use [18,19,20].”

    From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [20]

    The Methods identify this paper as a scoping review, and the Results describe its focus on AI models tested in a live clinical context without changing standard care. The Study design and purpose section reports variation in the designs and aims of the primary studies it reviewed. Our copy does not compare this review with the other reviews cited, so it does not establish that the reviews differ substantially from one another. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • Much of this literature addresses governance architectures, robustness concepts, implementation barriers, or monitoring frameworks, whereas only a subset is centred on longitudinal or operational evidence generated during routine deployment use.

    “Much of this literature addresses governance architectures, robustness concepts, implementation barriers, or monitoring frameworks, whereas only a subset is centred on longitudinal or operational evidence generated during routine deployment use [18,19,20].”

    From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [20]

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Existing reviews have examined specific dimensions of the problem, including post-deployment monitoring, temporal dataset shift, implementation frameworks, healthcare MLOps, governance, human-AI interaction, and deployment-proximal evaluation.

    “Existing reviews have examined specific dimensions of the problem, including post-deployment monitoring, temporal dataset shift, implementation frameworks, healthcare MLOps, governance, human–AI interaction, and deployment-proximal evaluation”

    From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [20]

  • Much of the review literature addressed governance architectures, robustness concepts, implementation barriers, monitoring frameworks, MLOps, or deployment-proximal evaluation, rather than mature evidence generated during sustained routine deployment.

    “Much of the review literature instead addressed governance architectures, robustness concepts, implementation barriers, monitoring frameworks, MLOps, or deployment-proximal evaluation, rather than mature evidence generated during sustained routine deployment [18,19,20,29,35,37,47,49].”

    From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [20]

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References 125 · Cited by 17

Research works printed but not linked: 35

  1. Sendak, M. et al. Editorial. Surfacing best practices for AI software development and integration in healthcare. Front. Digit. Health 5, 1150875 (2023).
  2. Escalé-Besa, A. et al. Exploring the potential of artificial intelligence in improving skin lesion diagnosis in primary care. Sci. Rep. 13, 4293 (2023).
  3. Tan, P., Nyeko-Lacek, M., Walsh, K., Sheikh, Z. & Lewis, C. J. Artificial intelligence-enhanced multispectral imaging for burn wound assessment: insights from a multi-centre UK evaluation. Burns 51, 107550 (2025).
  4. Afshar, M. et al. Deployment of real-time natural language processing and deep learning clinical decision support in the electronic health record: pipeline implementation for an opioid misuse screener in hospitalized adults. JMIR Med. Inform. 11, e44977 (2023).
  5. Sheppard, J. P. et al. Prospective external validation of the Predicting Out-of-OFfice Blood Pressure (PROOF-BP) strategy for triaging ambulatory monitoring in the diagnosis and management of hypertension: observational cohort study. BMJ 361, k2478 (2018).
  6. Wong, A. I. et al. Prediction of acute respiratory failure requiring advanced respiratory support in advance of interventions and treatment: a multivariable prediction model from electronic medical record data. Crit. Care Explor. 3, e0402 (2021).
  7. Razavian, N. et al. A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients. NPJ Digit. Med. 3, 130 (2020).
  8. Pou-Prom, C., Murray, J., Kuzulugil, S., Mamdani, M. & Verma, A. A. From compute to care: lessons learned from deploying an early warning system into clinical practice. Front. Digit. Health 4, 932123 (2022).
  9. Aakre, C. A., Kitson, J. E., Li, M. & Herasevich, V. Iterative user interface design for automated sequential organ failure assessment score calculator in sepsis detection. JMIR Hum. Factors 4, e14 (2017).
  10. Brajer, N. et al. Prospective and external evaluation of a machine learning model to predict in-hospital mortality of adults at time of admission. JAMA Netw. Open 3, e1920733 (2020).
  11. Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G. & King, D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 17, 195 (2019).
  12. Badgeley, M. A. et al. Deep learning predicts hip fracture using confounding patient and healthcare variables. NPJ Digit. Med. 2, 31 (2019).
  13. McCradden, M. et al. What's fair is… fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning: JustEFAB. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency 1505-1519 (Association for Computing Machinery, 2023); https://dl.acm.org/doi/ abs/10.1145/3593013.3594096 · DOI · Paper page
  14. Assadi, A. et al. An integration engineering framework for machine learning in healthcare. Front. Digit. Health 4, 932411 (2022).
  15. Covidence Systematic Review Software https://www.covidence. org (Veritas Health Innovation, 2025).
  16. Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N. & Folk, J. C. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit. Med. 1, 39 (2018).
  17. Tonekaboni, S. et al. How to validate machine learning models prior to deployment: silent trial protocol for evaluation of real- time models at ICU. In Proceedings of the Conference on Health, Inference, and Learning Vol. 174 (eds Flores, G. et al.) 169-182 (PMLR, 2022).
  18. Sendak, M. et al. "The human body is a black box": supporting clinical decision-making with deep learning. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency 99-109 (Association for Computing Machinery, 2020); https://doi. org/10.1145/3351095.3372827 Analysis https://doi.org/10.1038/s44360-025-00048-z · DOI · Paper page
  19. Balagopalan, A. et al. Machine learning for healthcare that matters: reorienting from technical novelty to equitable impact. PLOS Digit. Health 3, e0000474 (2024).
  20. Alrajhi, A. A. et al. Data-driven prediction for COVID-19 severity in hospitalized patients. Int. J. Environ. Res. Public Health 19, 2958 (2022).
  21. Aydın, E. et al. Diagnostic accuracy of a machine learning-derived appendicitis score in children: a multicenter validation study. Children (Basel) 12, 937 (2025).
  22. Bachelot, G. et al. A machine learning approach for the prediction of testicular sperm extraction in nonobstructive azoospermia: algorithm development and validation study. J. Med. Internet Res. 25, e44047 (2023).
  23. Berg, W. A. et al. Toward AI-supported US triage of women with palpable breast lumps in a low-resource setting. Radiology 307, e223351 (2023).
  24. Butler, H. J. et al. Development of high-throughput ATR-FTIR technology for rapid triage of brain cancer. Nat. Commun. 10, 4501 (2019).
  25. Cheng, Y. et al. Two-year hypertension incidence risk prediction in populations in the desert regions of northwest China: prospective cohort study. J. Med. Internet Res. 27, e68442 (2025).
  26. Chiang, D.-H., Jiang, Z., Tian, C. & Wang, C.-Y. Development and validation of a dynamic early warning system with time-varying machine learning models for predicting hemodynamic instability in critical care: a multicohort study. Crit. Care 29, 318 (2025).
  27. Chufal, K. S. et al. Machine learning model for predicting DIBH non-eligibility in left-sided breast cancer radiotherapy: development, validation and clinical impact analysis. Radiother. Oncol. 205, 110764 (2025).
  28. Felmingham, C. et al. Improving Skin cancer Management with ARTificial Intelligence (SMARTI): protocol for a preintervention/ postintervention trial of an artificial intelligence system used as a diagnostic aid for skin cancer management in a specialist dermatology setting. BMJ Open 12, e050203 (2022).
  29. Feng, W. et al. Identifying small thymomas from other asymptomatic anterior mediastinal nodules based on CT images using logistic regression. Front. Oncol. 15, 1590710 (2025).
  30. Liu, R. et al. Development and prospective validation of postoperative pain prediction from preoperative EHR data using attention-based set embeddings. NPJ Digit. Med. 6, 209 (2023).
  31. Liu, Y. et al. Validation of an established TW3 artificial intelligence bone age assessment system: a prospective, multicenter, confirmatory study. Quant. Imaging Med. Surg. 14, 144-159 (2024). 100. Luo, H. et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case- control, diagnostic study. Lancet Oncol. 20, 1645-1654 (2019). 101. Lupei, M. I. et al. A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19. PLoS ONE 17, e0262193 (2022). 102. Mahajan, A. et al. Development and validation of a machine learning model to identify patients before surgery at high risk for postoperative adverse events. JAMA Netw. Open 6, e2322285 (2023). 103. Major, V. J. & Aphinyanaphongs, Y. Development, implementation, and prospective validation of a model to predict 60-day end-of- life in hospitalized adults upon admission at three sites. BMC Med. Inform. Decis. Mak. 20, 214 (2020). 104. Miró Catalina, Q. et al. Real-world testing of an artificial intelligence algorithm for the analysis of chest X-rays in primary care settings. Sci. Rep. 14, 5199 (2024). 105. O'Brien, C. et al. Development, implementation, and evaluation of an in-hospital optimized early warning score for patient deterioration. MDM Policy Pract. 5, 2381468319899663 (2020). 106. Pan, Y. et al. An interpretable machine learning model based on optimal feature selection for identifying CT abnormalities in patients with mild traumatic brain injury. EClinicalMedicine 82, 103192 (2025). 107. Pyrros, A. et al. Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs. Nat. Commun. 14, 4039 (2023). 108. Qian, Y.-F., Zhou, J.-J., Shi, S.-L. & Guo, W.-L. Predictive model integrating deep learning and clinical features based on ultrasound imaging data for surgical intervention in intussusception in children younger than 8 months. BMJ Open 15, e097575 (2025). 109.
  32. Rawson, T. M. et al. Supervised machine learning to support the diagnosis of bacterial infection in the context of COVID-19. JAC Antimicrob. Resist. 3, dlab002 (2021). Analysis https://doi.org/10.1038/s44360-025-00048-z 110. · DOI
  33. Ren, L.-J. et al. Artificial intelligence assisted identification of newborn auricular deformities via smartphone application. EClinicalMedicine 81, 103124 (2025). 111. Schinkel, M. et al. Diagnostic stewardship for blood cultures in the emergency department: a multicenter validation and prospective evaluation of a machine learning prediction tool. EBioMedicine 82, 104176 (2022). 112. Shah, P. K. et al. A simulated prospective evaluation of a deep learning model for real-time prediction of clinical deterioration among ward patients. Crit. Care Med. 49, 1312-1321 (2021). 113. Shamout, F. E. et al. An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department. NPJ Digit. Med. 4, 80 (2021). 114. Shi, Y.-H. et al. Construction and validation of machine learning- based predictive model for colorectal polyp recurrence one year after endoscopic mucosal resection. World J. Gastroenterol. 31, 102387 (2025). 115. Smith, S. J., Bradley, S. A., Walker-Stabeler, K. & Siafakas, M. A prospective analysis of screen-detected cancers recalled and not recalled by artificial intelligence. J. Breast Imaging 6, 378-387 (2024). 116. Stamatopoulos, N. et al. Temporal and external validation of the algorithm predicting first trimester outcome of a viable pregnancy. Aust. N. Z. J. Obstet. Gynaecol. 65, 128-134 (2025). 117. Swinnerton, K. et al. Leveraging near-real-time patient and population data to incorporate fluctuating risk of severe COVID-19: development and prospective validation of a personalised risk prediction tool. EClinicalMedicine 81, 103114 (2025). 118. Tariq, A., Patel, B. N., Sensakovic, W. F., Fahrenholtz, S. J. & Banerjee, I. Opportunistic screening for low bone density using abdominopelvic computed tomography scans. Med. Phys. 50, 4296-4307 (2023). 119. Titano, J. J. et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat. Med. 24, 1337-1341 (2018). 120. Vaid, A. et al. Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation. J. Med. Internet Res. 22, e24018 (2020). 121.
  34. Wall, P. D. H., Hirata, E., Morin, O., Valdes, G. & Witztum, A. Prospective clinical validation of virtual patient-specific quality assurance of volumetric modulated arc therapy radiation therapy plans. Int. J. Radiat. Oncol. Biol. Phys. 113, 1091-1102 ( 20 22 ). 122. Wan, C.-F. et al. Radiomics of multimodal ultrasound for early prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer. Acad. Radiol. 32, 1861-1873 (2025). 123. Wang, X. et al. Prediction of the 1-year risk of incident lung cancer: prospective study using electronic health records from the state of Maine. J. Med. Internet Res. 21, e13260 (2019). 124. Wang, L., Wu, H., Wu, C., Shu, L. & Zhou, D. A deep-learning system integrating electrocardiograms and laboratory indicators for diagnosing acute aortic dissection and acute myocardial infarction. Int. J. Cardiol. 423, 133008 (2025). 125. Wissel, B. D. et al. Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery. Epilepsia 61, 39-48 (2020). 126. Xie, Z. et al. Enhanced diagnosis of axial spondyloarthritis using machine learning with sacroiliac joint MRI: a multicenter study. Insights Imaging 16, 91 (2025). 127. Ye, C. et al. A real-time early warning system for monitoring inpatient mortality risk: prospective study using electronic medical record data. J. Med. Internet Res. 21, e13719 (2019). 128. Ye, J.-Z. et al. Nomogram for prediction of the International Study Group of Liver Surgery (ISGLS) grade B/C posthepatectomy liver failure in HBV-related hepatocellular carcinoma patients: an external validation and prospective application study. BMC Cancer 20, 1036 (2020). 129. Yu, S. C. et al. Sepsis prediction for the general ward setting. Front. Digit. Health 4, 848599 (2022). 130. Zhang, Z. et al. Development of an MRI based artificial intelligence model for the identification of underlying atrial fibrillation after ischemic stroke: a multicenter proof-of-concept analysis. EClinicalMedicine 81, 103118 (2025). 131. Escalé-Besa, A. et al. Using artificial intelligence as a diagnostic decision support tool in skin disease: protocol for an observational prospective cohort study. JMIR Res. Protoc. 11, e37531 (2022). 132. Faqar-Uz-Zaman, S. F. et al. Study protocol for a prospective, double-blinded, observational study investigating the diagnostic accuracy of an app-based diagnostic health care application in an emergency room setting: the eRadaR trial. BMJ Open 11, e041396 (2021). 133. Felmingham, C. et al. Improving skin cancer management with ARTificial intelligence: a pre-post intervention trial of an artificial intelligence system used as a diagnostic aid for skin cancer management in a real-world specialist dermatology setting. J. Am. Acad. Dermatol. 88, 1138-1142 (2023). 134. Miró Catalina, Q., Fuster-Casanovas, A., Solé-Casals, J. & Vidal- Alaball, J. Developing an artificial intelligence model for reading chest x-rays: protocol for a prospective validation study. JMIR Res. Protoc. 11, e39536 (2022). 135.
  35. Sheppard, J. P., Martin, U., Gill, P., Stevens, R. & McManus, R. J. Prospective Register Of patients undergoing repeated OFfice and Ambulatory Blood Pressure Monitoring (PROOF-ABPM): protocol for an observational cohort study. BMJ Open 6, e012607 (2016).

Authors
Tikhomirov, L., Semmler, C., Prizant, N., Bhasin, S., Kenyon, G., van der Vegt, A., Erdman, L., Kurian, N. C., Thompson, H., Palmer, L. J., Mohamud, A., Gichoya, J. W., Soremekun, S., Sendak, M. P., Anderson, J. A., Pfohl, S. R., Stedman, I., Ehrmann, D., Verspoor, K., Kwong, J. C. C., Farmer, L.-A., London, A. J., Akrout, I., Joshi, S., Dicus, E., Liu, X., & McCradden, M. D.
Affiliations
  • Adelaide University (AU)
  • Duke University (US)
  • Cincinnati Children's Hospital Medical Center (US)
  • Sickkids Research Institute (CA)
  • Emory University (US)
  • London School of Hygiene & Tropical Medicine (GB)
  • University of Toronto (CA)
  • Google (United States) (US)
  • York University (CA)
  • University of Michigan (US)
  • The University of Melbourne (AU)
  • Carnegie Mellon University (US)
  • Hospital for Sick Children (CA)
  • Columbia University (US)
  • Central Adelaide Local Health Network (AU)
  • University of Birmingham (GB)
  • Women's and Children's Health Network (AU)
Corresponding author
Tikhomirov, Lana
ORCID
Venue
Nature Health
Publisher
Springer Science and Business Media LLC
Published
2026-02-16
Volume
1
Issue
5
Pages
532-554
ISSN
3005-0693 (ISSN-L)
DOI
10.1038/s44360-025-00048-z
OpenAlex
W7129041676
Cited by
17
References
125
Citations by year
2026: 16 · 2025: 1
FWCI
31.47
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Health (publishedVersion, cc-by)
  • University of Birmingham Research Portal (University of Birmingham) (publishedVersion, cc-by)
  • OSF Preprints (OSF Preprints) (submittedVersion, cc-by)
Funders
Hospital Research Foundation
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Keywords
AI evaluation, artificial intelligence in healthcare, clinical implementation of AI, stakeholder engagement, human-computer interaction, evaluation guidelines

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