Mapping Emerging Trends in Artificial Intelligence for Quality Control and Management: A Large‐Scale Computational Literature Review Using Semantic Natural Language Processing

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The integration of artificial intelligence into industrial quality control (QC) and management encompassing automated defect detection, real‐time process monitoring, and zero‐defect manufacturing has generated a rapidly expanding yet fragmented research landscape. This study addresses the need for comprehensive mapping of quality and reliability engineering practice through a computational systematic review of 9618 publications spanning the 2020–2025 period. A hybrid natural language processing (NLP) pipeline combining FlashText, Sentence‐BERT, KeyBERT, and spaCy performs semantic extraction of AI architectures, learning paradigms, defect typologies, and deployment contexts. The review pursues three objectives: mapping the architectural evolution of deep learning in industrial inspection, characterizing operational challenges of AI‐driven manufacturing, and identifying methodological frontiers for improving production reliability and supporting intelligent quality assurance (QA) systems. Results reveal that AI‐based quality control advances through coevolution of neural architectures, learning paradigms, and deployment requirements. CNNs remain central to discriminative visual inspection, while Transformers extend capabilities for multimodal reasoning. The field exhibits strong bias toward surface defects while overlooking safety‐critical internal defects. Emerging trends show transition from algorithm‐centric research toward deployment‐oriented paradigms including Uncertainty Quantification, Edge AI, Explainable AI, and Digital Twins. This work provides practitioners with an evidence‐based foundation for developing reliable, scalable AI quality systems aligned with Industry 4.0 principles.