Medical device manufacturers lose billions each year to quality control failures, driven by a common root cause: visual inspection systems that lack the data to learn from rare defects. With more than 13.8% increase in recalls in the previous year alone, the pressure to improve inspection accuracy and compliance has never been higher. Yet in most cases, datasets are flooded with “perfect” parts and critically low on real fault examples—making traditional AI training slow, costly, and unreliable.