Virtual services
Overview
Clinique Saint-Luc Bouge, a healthcare institution based in Namur, Belgium, is developing an AI-based solution to support rapid detection of urinary tract infections. Through TEF-Health, the hospital connected with the Centre d'Excellence en Technologies de l'Information et de la Communication (CETIC), whose engineers reviewed the end-to-end machine learning workflow behind the tool, from data preparation to deployment. The collaboration gave the hospital's team an external, technical perspective on how ready its system was for real-world clinical use, at no cost to the SME's own resources for building this expertise internally.
Impact
The assessment gave Clinique Saint-Luc Bouge a clearer, evidence-based view of its solution's technical maturity and the concrete steps needed to move toward safe real-world deployment, reducing the risk of costly missteps later in the process. Beyond the immediate technical findings, the hospital gained confidence in its broader data-driven clinical support strategy, and a template for how independent validation through a TEF can de-risk the journey from prototype to patient-facing tool.
For SMEs and clinical innovators elsewhere in Europe, the case illustrates how TEF-Health's testing infrastructure can reduce the time and uncertainty between a promising AI model and a solution ready for hospital floors.
The Challenge
Predictive AI tools built in clinical settings face a hurdle that has little to do with the accuracy of the underlying model: can the system be trusted to run reliably, reproducibly, and safely once it leaves the development environment? Clinique Saint-Luc Bouge needed an independent assessment of whether its urinary tract infection prediction software met the operational standards required for integration into hospital workflows, and where the gaps might lie.
The Solution
CETIC's engineers examined the solution's full machine learning pipeline through the lens of MLOps practice, the discipline concerned with keeping AI systems reproducible, traceable and maintainable once deployed. Working directly with the clinical team, they assessed data handling, model lifecycle management and the practical constraints of running AI software inside a hospital environment. The review produced concrete recommendations on data interoperability, the robustness of the model's predictive performance, and deployment practice specific to healthcare settings.
Explore how TEF-Health can help validate your AI or robotics solution
