Introducing Infrastructure as Code for deployment and governance of medical AI: A case study

Clinical AI deployment rarely reaches the bedside because both workstation integration and governed backend provisioning are challenging. We introduce a workflow that allows rapid and governed access to AI functionalities through a browser interface or API, enabling privacy-compliant data analysis. The complex setup for compliant healthcare applications such as a secure backend, private AI model endpoint, and managed identity configuration is encoded by itself, a concept known as Infrastructure as Code (IaC) . IaC is a concept in which networks, access rights, and service endpoints are written down as machine-readable files that can be reviewed once and executed identically in any institution. Here, we apply this concept to a clinical use case. The process of deploying a governed, private clinical AI solution using this approach in a tertiary care center took, on average, 310.3 ± 196.6 seconds (n=25, mean ± SD) . To further demonstrate the clinical value of this approach, we developed and deployed an LLM-based inconsistency checker, which identifies textual inconsistency in medical documents. When evaluating the tool on a set of 1,347 physician-approved clinical documents, the tool flagged a clinically severe inconsistency in 105 clinical documents (7 . 8%, approximately 1 in 13) . In a severity-stratified physician audit, 149 of 150 sampled findings were confirmed (population-weighted PPV 98 . 2%; CI 94 . 8 - 100 . 0) . In summary, we present the application of IaC frameworks to a healthcare use case and demonstrate that clinically useful AI applications can be deployed to physician workstations in hospital-governed cloud environments within a few minutes.