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

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.