Under CopyrightRoscher, KarstenKarstenRoscher2022-03-1410.8.20212021https://publica.fraunhofer.de/handle/publica/41176510.24406/publica-fhg-411765Automated visual inspection based on machine learning and computer vision algorithms is a promising approach to ensure the quality of critical medical implants and equipments. However, limited availability of data and potentially unpredictable deep learning models pose major challenges to bring such solutions to life and to the market. This talk addresses the open challenges as well as current research directions for dependable visual inspection in quality assurance of medical products.enartificial intelligenceAIsafetySafe Artificial IntelligenceSafe Intelligencemachine learningMLdeep learningcomputer visionquality assuranceuncertainty estimationActive Learningmedical technologypresentation