Biostatistics and Reliability Engineering in Medical AI Evaluation

Medical AI systems operate in high-stakes domains where failure carries direct consequences for patient safety. Rigorous evaluation must integrate biostatistical methods with reliability engineering principles. I synthesize agreement statistics (Fleiss' kappa, Krippendorff's alpha), error taxonomies, and failure-mode analysis into a unified framework for assessing AI clinical performance. A critical distinction emerges between *aleatory* uncertainty—stochastic variation inherent to diagnosis—and *epistemic* uncertainty, which represents systematic model gaps. I pair statistical methods for quantifying rater agreement with engineering approaches: failure-rate modeling, confidence calibration analysis, and sensitivity testing. This combined view produces a comprehensive picture of AI system reliability. Through clinical case examples, I show how agreement metrics alone miss systematic failure modes, while taxonomic classification of errors enables targeted improvement. The framework applies to any clinical domain where humans and AI jointly inspect and grade system outputs.