High-Precision OCR for Medical Device Labeling with RF-DETR and Gemini 2.5 Flash

Ensuring the integrity of medical hardware involves leveraging computer vision and AI to automate the verification of Optical Character Recognition (OCR) on device displays and labels, a crucial aspect of quality control in healthcare manufacturing. Historically reliant on manual inspections prone to human error, the process now employs AI-powered systems to detect labeling inconsistencies around the clock. The implementation involves a two-stage approach using an RF-DETR model for detecting critical metrics and a Vision Agent with Gemini 2.5 Flash for OCR and clinical validation, ensuring zero-error manufacturing. By training the RF-DETR model with a specialized dataset and applying preprocessing and augmentation techniques, manufacturers can achieve high precision and recall rates, reducing the risk of medical device recalls due to labeling errors. The workflow extends beyond training to incorporate a Roboflow Workflow, which structures the process from detection to reasoning, ultimately converting AI responses into standardized data for factory dashboards and ensuring that medical devices display accurate and legible data.