A scoping review of silent trials for medical artificial intelligence
Chen, J. H. & Asch, S. M. Machine learning and prediction in medicine—beyond the peak of inflated expectations. N. Engl. J. Med. 376, 2507–2509 (2017).
Google Scholar
Seneviratne, M. G., Shah, N. H. & Chu, L. Bridging the implementation gap of machine learning in healthcare. BMJ Innov. 6, 45–47 (2020).
Google Scholar
Sendak, M. P. et al. A path for translation of machine learning products into healthcare delivery. EMJ Innov. (2020).
Google Scholar
McCradden, M. D., Stephenson, E. A. & Anderson, J. A. Clinical research underlies ethical integration of healthcare artificial intelligence. Nat. Med. 26, 1325–1326 (2020).
Google Scholar
Sendak, M. et al. Editorial. Surfacing best practices for AI software development and integration in healthcare. Front. Digit. Health 5, 1150875 (2023).
Google Scholar
Wiens, J. et al. Do no harm: a roadmap for responsible machine learning for health care. Nat. Med. 25, 1337–1340 (2019).
Google Scholar
Morse, K. E., Bagley, S. C. & Shah, N. H. Estimate the hidden deployment cost of predictive models to improve patient care. Nat. Med. 26, 18–19 (2020).
Google Scholar
McCradden, M. D. et al. A research ethics framework for the clinical translation of healthcare machine learning. Am. J. Bioeth. 22, 8–22 (2022).
Google Scholar
Futoma, J., Simons, M., Panch, T., Doshi-Velez, F. & Celi, L. A. The myth of generalisability in clinical research and machine learning in health care. Lancet Digit. Health 2, e489–e492 (2020).
Google Scholar
Kim, C. et al. Multicentre external validation of a commercial artificial intelligence software to analyse chest radiographs in health screening environments with low disease prevalence. Eur. Radiol. 33, 3501–3509 (2023).
Google Scholar
Wong, A. et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern. Med. 181, 1065–1070 (2021).
Google Scholar
Harvey, H. B. & Gowda, V. How the FDA regulates AI. Acad. Radiol. 27, 58–61 (2020).
Google Scholar
Wu, E. et al. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat. Med. 27, 582–584 (2021).
Google Scholar
McCradden, M. D. et al. CANAIRI: the Collaboration for Translational Artificial Intelligence Trials in healthcare. Nat. Med. 31, 9–11 (2025).
Google Scholar
Arksey, H. & O’Malley, L. Scoping studies: towards a methodological framework. Int. J. Soc. Res. Methodol. 8, 19–32 (2005).
Google Scholar
Manz, C. R. et al. Validation of a machine learning algorithm to predict 180-day mortality for outpatients with cancer. JAMA Oncol. 6, 1723–1730 (2020).
Google Scholar
Kwong, J. C. C. et al. When the model trains you: induced belief revision and its implications on artificial intelligence research and patient care—a case study on predicting obstructive hydronephrosis in children. NEJM AI 1, AIcs2300004 (2024).
Google Scholar
Sendak, M. P. et al. Real-world integration of a sepsis deep learning technology into routine clinical care: implementation study. JMIR Med. Inform. 8, e15182 (2020).
Google Scholar
Jauk, S. et al. Risk prediction of delirium in hospitalized patients using machine learning: an implementation and prospective evaluation study. J. Am. Med. Inform. Assoc. 27, 1383–1392 (2020).
Google Scholar
Stephen, R. J. et al. Sepsis prediction in hospitalized children: clinical decision support design and deployment. Hosp. Pediatr. 13, 751–759 (2023).
Google Scholar
Aakre, C. et al. Prospective validation of a near real-time EHR-integrated automated SOFA score calculator. Int. J. Med. Inform. 103, 1–6 (2017).
Google Scholar
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2025); https://www.r-project.org/
Posit Team. RStudio: Integrated Development Environment for R (Posit Software, PBC, 2025).
Escalé-Besa, A. et al. Exploring the potential of artificial intelligence in improving skin lesion diagnosis in primary care. Sci. Rep. 13, 4293 (2023).
Google Scholar
Rajakariar, K. et al. Accuracy of a smartwatch based single-lead electrocardiogram device in detection of atrial fibrillation. Heart 106, 665–670 (2020).
Google Scholar
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).
Google Scholar
Morse, K. E. et al. Monitoring approaches for a pediatric chronic kidney disease machine learning model. Appl. Clin. Inform. 13, 431–438 (2022).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Ganapathi, S. et al. Tackling bias in AI health datasets through the STANDING Together initiative. Nat. Med. 28, 2232–2233 (2022).
Google Scholar
Ouyang, D. et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature 580, 252–256 (2020).
Google Scholar
Razavian, N. et al. A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients. NPJ Digit. Med. 3, 130 (2020).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Nemeth, C. et al. TCCC decision support with machine learning prediction of hemorrhage risk, shock probability. Mil. Med. 188, 659–665 (2023).
Google Scholar
Shelov, E. et al. Design and implementation of a pediatric ICU acuity scoring tool as clinical decision support. Appl. Clin. Inform. 9, 576–587 (2018).
Google Scholar
Bedoya, A. D. et al. Machine learning for early detection of sepsis: an internal and temporal validation study. JAMIA Open 3, 252–260 (2020).
Google Scholar
Artificial Intelligence/Machine Learning-enabled Working Group. Good Machine Learning Practice for medical device development: guiding principles. FDA (2025).
DECIDE-AI Steering Group. DECIDE-AI: new reporting guidelines to bridge the development-to-implementation gap in clinical artificial intelligence. Nat. Med. 27, 186–187 (2021).
Google Scholar
Rivera, S. C. et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Lancet Digit. Health 2, e549–e560 (2020).
Google Scholar
Liu, X. et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit. Health 2, e537–e548 (2020).
Google Scholar
Moher, D. Guidelines for reporting health care research: advancing the clarity and transparency of scientific reporting. Can. J. Anaesth. 56, 96–101 (2009).
Google Scholar
Evidence standards framework for digital health technologies. Section C: evidence standards tables. NICE (2018).
Gaube, S. et al. Do as AI say: susceptibility in deployment of clinical decision-aids. NPJ Digit. Med. 4, 31 (2021).
Google Scholar
Chromik, M., Eiband, M., Buchner, F., Krüger, A. & Butz, A. I think I get your point, AI! The illusion of explanatory depth in explainable AI. In 26th International Conference on Intelligent User Interfaces 307–317 (Association for Computing Machinery, 2021); https://doi.org/10.1145/3397481.3450644
Felmingham, C. M. et al. The importance of incorporating human factors in the design and implementation of artificial intelligence for skin cancer diagnosis in the real world. Am. J. Clin. Dermatol. 22, 233–242 (2021).
Google Scholar
Tikhomirov, L. et al. Medical artificial intelligence for clinicians: the lost cognitive perspective. Lancet Digit. Health 6, e589–e594 (2024).
Google Scholar
Park, Y. et al. Evaluating artificial intelligence in medicine: phases of clinical research. JAMIA Open 3, 326–331 (2020).
Google Scholar
Lemmon, J. et al. Evaluation of feature selection methods for preserving machine learning performance in the presence of temporal dataset shift in clinical medicine. Methods Inf. Med. 62, 60–70 (2023).
Google Scholar
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).
Google Scholar
Finlayson, S. G. et al. The clinician and dataset shift in artificial intelligence. N. Engl. J. Med. 385, 283–286 (2021).
Google Scholar
Badgeley, M. A. et al. Deep learning predicts hip fracture using confounding patient and healthcare variables. NPJ Digit. Med. 2, 31 (2019).
Google Scholar
Bozkurt, S. et al. Reporting of demographic data and representativeness in machine learning models using electronic health records. J. Am. Med. Inform. Assoc. 27, 1878–1884 (2020).
Google Scholar
Plana, D. et al. Randomized clinical trials of machine learning interventions in health care: a systematic review. JAMA Netw. Open 5, e2233946 (2022).
Google Scholar
Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 447–453 (2019).
Google Scholar
McCradden, M. D., Joshi, S., Mazwi, M. & Anderson, J. A. Ethical limitations of algorithmic fairness solutions in health care machine learning. Lancet Digit. Health 2, e221–e223 (2020).
Google Scholar
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
Gichoya, J. W. et al. AI recognition of patient race in medical imaging: a modelling study. Lancet Digit. Health 4, e406–e414 (2022).
Google Scholar
Arora, A. et al. The value of standards for health datasets in artificial intelligence-based applications. Nat. Med. 29, 2929–2938 (2023).
Google Scholar
McCradden, M. D. et al. What makes a ‘good’ decision with artificial intelligence? A grounded theory study in paediatric care. BMJ Evid. Based Med. 30, 183–193 (2025).
Google Scholar
Assadi, A. et al. An integration engineering framework for machine learning in healthcare. Front. Digit. Health 4, 932411 (2022).
Google Scholar
Militello, L. G. et al. Using human factors methods to mitigate bias in artificial intelligence-based clinical decision support. J. Am. Med. Inform. Assoc. 32, 398–403 (2025).
Google Scholar
Campbell, N. C. et al. Designing and evaluating complex interventions to improve health care. BMJ 334, 455–459 (2007).
Google Scholar
Aromataris, E. et al. (eds) JBI Manual for Evidence Synthesis (JBI, 2024); https://synthesismanual.jbi.global
Tricco, A. et al. PRISMA extension for scoping reviews (PRISMAScR): checklist and explanation. Ann. Intern. Med. 169, 467–473 (2018).
Google Scholar
Breiman, L. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16, 199–231 (2001).
Google Scholar
Covidence Systematic Review Software (Veritas Health Innovation, 2025).
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).
Google Scholar
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).
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
Balagopalan, A. et al. Machine learning for healthcare that matters: reorienting from technical novelty to equitable impact. PLOS Digit. Health 3, e0000474 (2024).
Google Scholar
Papoutsi, C., Wherton, J., Shaw, S., Morrison, C. & Greenhalgh, T. Putting the social back into sociotechnical: case studies of co-design in digital health. J. Am. Med. Inform. Assoc. 28, 284–293 (2021).
Google Scholar
Alrajhi, A. A. et al. Data-driven prediction for COVID-19 severity in hospitalized patients. Int. J. Environ. Res. Public Health 19, 2958 (2022).
Google Scholar
Aydın, E. et al. Diagnostic accuracy of a machine learning-derived appendicitis score in children: a multicenter validation study. Children (Basel) 12, 937 (2025).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Butler, H. J. et al. Development of high-throughput ATR-FTIR technology for rapid triage of brain cancer. Nat. Commun. 10, 4501 (2019).
Google Scholar
Campanella, G. et al. Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection. Nat. Med. 31, 3002–3010 (2025).
Google Scholar
Chen, Y. et al. Endoscopic ultrasound-based radiomics for predicting pathologic upgrade in esophageal low-grade intraepithelial neoplasia. Surg. Endosc. 39, 2239–2249 (2025).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Coley, R. Y., Walker, R. L., Cruz, M., Simon, G. E. & Shortreed, S. M. Clinical risk prediction models and informative cluster size: assessing the performance of a suicide risk prediction algorithm. Biom. J. 63, 1375–1388 (2021).
Google Scholar
Corbin, C. K. et al. DEPLOYR: a technical framework for deploying custom real-time machine learning models into the electronic medical record. J. Am. Med. Inform. Assoc. 30, 1532–1542 (2023).
Google Scholar
Dave, C. et al. Prospective real-time validation of a lung ultrasound deep learning model in the ICU. Crit. Care Med. 51, 301–309 (2023).
Google Scholar
El Moheb, M. et al. An open-architecture AI model for CPT coding in breast surgery: development, validation, and prospective testing. Ann. Surg. 282, 439–448 (2025).
Google Scholar
Faqar-Uz-Zaman, S. F. et al. The diagnostic efficacy of an app-based diagnostic health care application in the emergency room: eRadaR-trial. A prospective, double-blinded, observational study. Ann. Surg. 276, 935–942 (2022).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Hanley, D. et al. Emergency department triage of traumatic head injury using a brain electrical activity biomarker: a multisite prospective observational validation trial. Acad. Emerg. Med. 24, 617–627 (2017).
Google Scholar
Hoang, M. T. et al. Evaluating the utility of a clinical sepsis AI tool in emergency waiting rooms: a preliminary silent trial. Stud. Health Technol. Inform. 329, 307–311 (2025).
Google Scholar
Im, H. et al. Design and clinical validation of a point-of-care device for the diagnosis of lymphoma via contrast-enhanced microholography and machine learning. Nat. Biomed. Eng. 2, 666–674 (2018).
Google Scholar
Korfiatis, P. et al. Automated artificial intelligence model trained on a large data set can detect pancreas cancer on diagnostic computed tomography scans as well as visually occult preinvasive cancer on prediagnostic computed tomography scans. Gastroenterology 165, 1533–1546 (2023).
Google Scholar
Kramer, D. et al. Machine learning-based prediction of malnutrition in surgical in-patients: a validation pilot study. Stud. Health Technol. Inform. 313, 156–157 (2024).
Google Scholar
Kwong, J. C. C. et al. The silent trial—the bridge between bench-to-bedside clinical AI applications. Front. Digit. Health 4, 929508 (2022).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Pyrros, A. et al. Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs. Nat. Commun. 14, 4039 (2023).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Ren, L.-J. et al. Artificial intelligence assisted identification of newborn auricular deformities via smartphone application. EClinicalMedicine 81, 103124 (2025).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Titano, J. J. et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat. Med. 24, 1337–1341 (2018).
Google Scholar
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).
Google Scholar
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 (2022).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Xie, Z. et al. Enhanced diagnosis of axial spondyloarthritis using machine learning with sacroiliac joint MRI: a multicenter study. Insights Imaging 16, 91 (2025).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Yu, S. C. et al. Sepsis prediction for the general ward setting. Front. Digit. Health 4, 848599 (2022).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
link
