The article investigates the application of vision systems, enhanced by advanced image processing algorithms, in quality inspection processes for medical instruments, addressing the limitations of traditional manual inspection methods. The study focuses on the use of a Keyence vision system equipped with advanced image processing algorithms and LumiTrax technology to detect defects in surgical needle holders. A pilot study was conducted, developing and verifying five inspection programs through controlled experiments with 75 test samples containing known defects. The results demonstrated high effectiveness of the vision system, achieving 100% accuracy in detecting solder and joint defects, although challenges were observed in identifying surface defects, leading to two Type II errors. Compared to manual inspection, the automated system significantly enhanced speed, repeatability, and accuracy while reducing inspection time. The research highlights the practical implications of integrating vision systems into manufacturing processes, particularly in high-responsibility sectors such as the medical industry. By reducing reliance on labor-intensive methods, minimizing material waste, and ensuring defect detection, the implementation of such systems supports sustainable manufacturing practices.