A Generalizable Three-Pillar Informatics Methodology for Safety-Critical Clinical AI APIs: Validation in Cardiology

A Generalizable Three-Pillar Informatics Methodology for Safety-Critical Clinical AI APIs: Validation in Cardiology | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Generalizable Three-Pillar Informatics Methodology for Safety-Critical Clinical AI APIs: Validation in Cardiology Behailu Getachew Wolde, Ferede Ali Tahir, Solomon Gebremeskel Adane This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8621658/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper presents a novel informatics methodology for designing clinical AI APIs that integrates behavior-driven validation, adaptive physiological thresholding, and automated regulatory compliance into a unified, generalizable framework. The approach is designed to bridge the gap between algorithmic performance and real-world clinical reliability in safety-critical domains. We evaluate the methodology in cardiology using 12,427 ECG samples from the MIT-BIH Arrhythmia Database, achieving 95% sensitivity in arrhythmia detection. A cloud-native implementation demonstrates 99.99% API availability under 10,000 concurrent emergency workflows, with end-to-end diagnostic latency reduced by 62% to 320ms. The framework’s core contribution is methodological: a reproducible three-pillar architecture that ensures clinical plausibility, regulatory adherence, and operational resilience—providing a transferable model for AI integration across high-stakes clinical environments. Clinical Informatics API Methodology Adaptive Validation Regulatory Automation Cloud Resilience ECG Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8621658","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576065185,"identity":"9f441f89-cbdb-4181-a1bf-190b5c684a52","order_by":0,"name":"Behailu Getachew 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