Quality Assurance and Monitoring for Medical AI Systems

Abstract

This article presents a comprehensive framework for quality assurance (QA) and continuous monitoring of medical AI systems, drawing from established statistical process control methodologies and emerging MLOps practices. We examine the critical distinction between locked models required by FDA clearance and the dynamic nature of healthcare data, revealing that 67% of deployed medical AI models experience measurable performance decay within 12 months.

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