US Pharma MarketingAI-enabled medical devices might perform well during testing but some could still fail when used on real-world patients whose medical images differ from the data used to train the underlying models, according to a new Paragon Health Institute report examining "generalization uncertainty" in healthcare AI systems.
AI-enabled medical devices may fail on real-world patients, report cautions
Unlike traditional software systems that rely on deterministic rules, artificial intelligence devices frequently use predictive models trained on specific datasets. AI-enabled medical devices might perform well during testing but some could still fail when used on real-world patients whose medical images differ from the data used to train the underlying models, according to a new Paragon Health Institute report examining "generalization uncertainty" in healthcare AI systems. The paper describes generalization as an AI device’s ability to process and interpret real-world data accurately outside controlled testing environments, arguing failures can create patient-safety risks, undermine clinician confidence and slow broader adoption of…
Source: Healthcare IT News