要約
Machine learning operations (MLOps) practices have reached a notable level of maturity within general-purpose software engineering. Pipelines are standardized, monitoring is automated, and deployment patterns are well rehearsed. Yet when these same practices are transferred into clinical environments, their adequacy becomes less certain. The healthcare domain introduces operational and ethical pressures that conventional MLOps frameworks were not originally designed to address.
Clinical AI systems operate under constraints that extend beyond typical production settings. Stringent data protection regimes — including GDPR and the EU AI Act — shape how data can be processed and retained. Model drift is not merely a statistical inconvenience; it may alter clinical decisions with tangible consequences for patient outcomes. Regulatory compliance demands comprehensive, tamper-evident audit trails. At the same time, decision-critical contexts impose a clear expectation of human oversight. In other words, technical robustness alone is insufficient; governance and accountability become integral system properties.
This paper introduces a Clinical MLOps framework that systematically augments conventional pipelines with healthcare-specific requirements. The framework is structured around four layered components: (1) privacy-preserving deployment patterns, (2) clinical observability mechanisms, (3) compliance-oriented audit trail architecture, and (4) human-in-the-loop governance protocols. Each layer addresses a distinct operational risk while remaining interoperable with established cloud-native tooling.
To evaluate the framework, we construct a demonstrative end-to-end pipeline using the MIMIC-IV dataset — a large, de-identified electronic health record repository from Beth Israel Deaconess Medical Center. Within this pipeline, we implement a patient deterioration prediction model and apply Clinical MLOps controls systematically at every stage, from data ingestion to post-deployment monitoring. This controlled implementation enables us to examine how standard MLOps tooling behaves under clinical constraints.
Our findings indicate that widely adopted MLOps practices, while technically sound, leave critical governance and compliance gaps when applied in healthcare contexts. The proposed Clinical MLOps framework addresses these deficiencies without requiring impractical infrastructure changes. Importantly, it remains compatible with cloud-native architectures, making adoption feasible within contemporary health IT ecosystems. The implications extend to healthcare AI governance more broadly, particularly regarding the operational interpretation of the EU AI Act in clinical settings.
