Regulatory bodies like the FDA and MHRA are adapting guidelines to accommodate the evolving AI landscape, stressing the importance of innovative compliance approaches alongside traditional software regulations, including audit trails, electronic records, and signatures.
Integrating AI into quality control processes requires a thoughtful approach that goes beyond mere technology adoption. Here are some proven strategies to ensure successful AI empowerment in quality control.
The recent Boeing door plug failures highlight the urgent need for better quality control. Utilizing AI, particularly Large Vision Models (LVMs), offers a promising solution for enhancing quality assurance by providing unparalleled precision, efficiency, and scalability.
Quality 4.0 looks at how digitization improves industry processes. Despite over 10 years of focus, there's still no clear definition or knowledge base for Quality 4.0.
Fatigue testing has made significant progress in recent years, especially in test and environmental conditions. Remote monitoring has advanced with the use of AI-enabled camera systems, making it easier to integrate legacy instruments.
Quality assurance (QA) meets artificial intelligence (AI). How can they coexist safely? Integrating AI into QA brings opportunities and risks, especially in safety-critical environments. Balancing rigorous standards and human oversight is crucial.
Quality professionals use deep learning AI to quickly identify and prevent significant product defects, bringing a substantial leap forward in quality assurance and process improvement. Unlike traditional automation controllers, deep learning allows computers to proactively grow their knowledge base and adapt to evolving circumstances.