{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T16:42:50Z","timestamp":1792428170046,"version":"4.5.0"},"reference-count":63,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Res Protoc"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n In-hospital cardiac arrest (IHCA) remains a frequent and devastating event, and conventional early warning scores (EWSs) coupled with rapid response systems (RRSs) have shown inconsistent effects on patient-centered outcomes, largely owing to limited predictive accuracy and high false-alarm burden. AI-based EWSs (AI-EWSs) have demonstrated superior discrimination retrospectively, but high-quality prospective evidence in true external validation environments remains scarce. Existing AI-EWS trials have either been confined to internal-development settings or bundled the algorithm with specific implementation infrastructure, leaving the stand-alone effect of AI-based software as a medical device (AI-SaMD) on patient outcomes uncharacterized.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n \n This trial will evaluate whether implementation of AI-SaMD (VUNO Med\n \n -DeepCARS\n \n ) as a stand-alone clinical decision-support tool in routine general-ward care reduces IHCA compared with usual care based on conventional EWSs.\n <\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n The VITAL-MD trial (validation of an AI-based clinical deterioration prediction model on patient outcomes through a stepped-wedge cluster randomized trial [SW-CRT] multicenter evaluation of DeepCARS) is a pragmatic, multicenter, SW-CRT conducted at 4 tertiary academic hospitals in the Republic of Korea. Six tertiary academic hospitals were assessed for eligibility based on inpatient capacity, RRS operating hours aligned with the national RRS pilot program, and no prior AI-SaMD use. All adult patients aged 19 years or older admitted to general wards during the 12-month trial period are included; patients younger than 19 years, or with no documented vital signs throughout hospitalization, are excluded. The 4 sites are randomized to sequentially transition from preimplementation (usual care) to postimplementation (AI-SaMD\u2013guided care) in 4 steps separated by 7-day washout periods. DeepCARS\n \n generates real-time risk scores from 4 vital signs and integrates into the existing electronic medical record interface without altering RRS workflows. The primary outcome is the incidence of general-ward IHCA, defined per the Utstein guidelines, among the target cohort (defined as patients exceeding the alert threshold at least once). Assuming a baseline IHCA incidence of 6.70%, a 40% relative risk reduction, an intracluster correlation coefficient of 0.01, and 350 patients per cluster-period, 7002 target-cohort patients (total enrollment approximately 72,695) provide 80% power. Analyses follow the intention-to-treat principle using generalized linear mixed models adjusting for prespecified covariates, with prespecified sensitivity, subgroup, and compliance analyses.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The trial was registered with the Clinical Research Information Service (KCT0010243) on February 28, 2025. Recruitment was conducted from April 1, 2025, to March 31, 2026. All 4 sites completed transition to the postimplementation period, and data cleaning is currently underway before the final database lock, with primary results expected in 2027.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n VITAL-MD will provide the first large-scale, multicenter randomized evidence evaluating whether an AI-EWS, deployed as a stand-alone device in external validation environments and benchmarked against real-world standard-of-care practice, improves patient-centered outcomes for general-ward patients, advancing the evaluation of AI-SaMD from retrospective diagnostic accuracy toward prospective, action-anchored clinical utility.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/104230","type":"journal-article","created":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T16:22:13Z","timestamp":1792426933000},"page":"e104230-e104230","source":"Crossref","is-referenced-by-count":0,"title":["Clinical Effectiveness of AI-Based Cardiac Arrest Prediction System for 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