Artificial Intelligence’s contribution to Quality 4.0: results from a systematic literature review

Abstract

Artificial Intelligence (AI) has become a widely explored topic in recent years, and its association with Quality 4.0 has gained significant importance. This paper aims to understand how AI technologies can contribute to quality improvement and support the transition toward Quality 4.0, specifically focusing on quality control and assurance in industrial environments. Using the PRISMA methodology, a systematic literature review was conducted to identify key trends, challenges and opportunities in applying AI to quality systems. The initial search yielded 835 records. After applying refinement criteria to focus on empirical studies, 205 papers were considered eligible and 89 articles were selected for content analysis. The findings identify critical challenges, including data availability, reproducibility and quality, which affect the precision and reliability of AI systems. AI applications were found across different industries, particularly in real-time monitoring, predictive analytics and decision support. Approximately 80% of the reviewed studies focused on AI-supported real-time decision-making. Deep Learning, Computer Vision and Machine Learning emerged as the most frequently used technologies. Sustainable Development Goal 9 – Industry, Innovation and Infrastructure – was addressed in 88% of the selected papers. Future research should address the identified challenges and further explore AI applications in manufacturing quality control.