This paper presents a technical assessment of the regulatory complexities associated with implementing a deep learning (DL)-based automated visual inspection system for Class III medical devices. Specifically, it examines the challenges manufacturers are likely to face, as the high-risk system requirements under the EU AI Act differ from existing regulatory frameworks such as the Medical Device Regulation (MDR) and the FDA Quality Standards Regulation (QSR). It analyzes differences in risk management principles, dataset governance, model validation, explainability requirements, and post-deployment monitoring obligations, and highlights areas of uncertainty, including data retention burdens, global compliance challenges, and the difficulty of achieving statistical significance in validation with limited defect data. The discussion focuses on static models and offers a technical perspective, rather than providing legal or regulatory advice.
