Case Study: From AI Evaluation to Impact in One Month

Selecting radiology AI on vendor claims or pooled literature alone is risky. Performance varies by site due to scanners, acquisition protocols, patient mix, and prevalence, which can alter how closely real-world cases match the data used to develop the model. Even when devices meet regulatory requirements, published accuracy often fails to predict workflow fit or value for a specific service line. A vendor-neutral evaluation on local data is therefore the responsible path.

Diagnostikum Linz, a high-volume outpatient provider, compared multiple chest X-ray (CXR) AI solutions on the same retrospective cases and selected the profile that best matched its case mix and throughput goals. Despite some AI solutions performing better across a mix of cases, Diagnostikum’s AI choice prioritized value, choosing to center on safely accelerating normal-case handling with the Oxipit CXR Suite, so radiologists could focus more time on abnormal cases.

Using the deepcOS® AI Evaluator, Diagnostikum moved from evaluation to adoption in about one month, materially accelerating time to value within the CXR workflow, setting the organization up to save over 1,000 radiologist hours per year.