Clinical Effectiveness of an Artificial Intelligence-Based Prediction Model for Cardiac Arrest in General Ward-Admitted Patients: A Non-Randomized Controlled Trial.

ABSTRACT: Background: Ward patients who experience clinical deterioration are at high risk of mortality. Conventional rapid response systems (RRS) using track-and-trigger protocols have not consistently demonstrated improved outcomes. This study evaluated the impact of an artificial intelligence (AI)-based cardiac arrest prediction model. Methods: This 1-year, prospective, non-randomized interventional trial assigned hospitalized patients with AI-based software as a medical device (AI-SaMD) high-risk alerts to groups based on their subsequent clinical response; those reassessed or treated within 24 h comprised the AI-SaMD-guided cohort, while the remainder formed the usual care cohort. Alerts prompted an optional but not mandatory treatment review. The primary outcome was ward-based ca

SUBMITTER: Park MH 

PROVIDER: S-EPMC12839744 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature