DevOps for Diagnostics: Automated Continuous Integration/Continuous Deployment (CI/CD) of Multi-Modal AI Models into Hospital PACS and LIS

Abstract

Diagnostic healthcare has been confronted with a consistent problem of deploying multi-modal models of artificial intelligence into the current systems of Picture Archiving and Communication Systems (PACS) and Laboratory Information Systems (LIS) because of the long-term implementation cycle, version management difficulties, and workflow interruptions. Continuous Integration/Continuous Deployment pipelines based on DevOps are radically innovative in that they automate the entire lifecycle of diagnostic AI models, starting with their creation up to their on-production maintenance. Kubernetes-based cloud-native architectures have the scalability, resilience, and compute efficiency required to handle computationally intensive diagnostic loads, as well as to regulate and govern data. The proposed framework will enforce automatic model versioning, drift detection policies, multi-phase validation policies, and a smooth integration with hospital enterprise systems via DICOM and HL7 interfaces. Empirical assessment in various healthcare facilities proves significant success rates in deployments, system stability, and system processing time without affecting clinical operations and diagnostic results during automated retraining loops. Incremental deployment models, such as canary releases and full monitoring infrastructure, allow safe updating of models without interfering with running clinical processes. Federated learning has the capability of integrating to enable the multi-institutional models to become improved, coupled with privacy preservation and regulation limitations on data sharing. Clinical acceptance evaluations indicate that there is little workflow interference and a gradual increase in user confidence due to the coherent model activity and dependability. The framework is effective in considering the main issue of long-term effectiveness because over time, the population of patients, the prevalence of the disease, and clinical practices may change. Computerized governance protocols and model registry centralized offer institutional supervision and decrease the IT support load, and facilitate the scalable implementation of AI-powered diagnostics in heterogeneous healthcare settings.