AI models of unstable flow exhibit hallucination.

High‐performance ML models for clinical risk prediction face a well‐documented translational barrier: even when performance metrics satisfy publication standards, models rarely reach the clinical workflow [, ]. A systematic review of 84 FDA‐cleared AI/ML‐based medical devices found that fewer than 30% included prospective clinical validation evidence, and fewer than 10% reported deployment architecture details sufficient for independent replication []. Barriers to deployment include: privacy concerns about processing patient data in cloud systems, inability to handle incomplete real‐world inputs, lack of interpretable explanations for non‐ML‐trained clinicians, and the complexity of deploying and maintaining inference infrastructure at health‐system scale []. Infrastructure complexity is particularly acute for partition‐first ensemble models: up to serialized base learners (2 cohorts × 7 age bands × 4 density bins × 3 algorithms), organized as bin‐level weighted ensembles, must be accessible at inference time. Each ensemble was trained independently per utilization‐density stratum (25‐split MCCV + Optuna per bin). The serverless architecture collapses these artifacts into a single Lambda container with in‐process routing, eliminating inter‐service API latency overhead. PGx testing results are personal genomic data requiring HIPAA‐compliant handling, yet existing tools (CPIC lookup systems, institutional pharmacogenomics portals) are siloed with no integration of model‐based prediction []. The PREDICT program [] demonstrated 10‐year sustainability of EHR‐embedded PGx CDS, but required institutional EHR integration unavailable to community pharmacies and Safety Net hospitals.