AI in Quality Assurance: A Systematic Review

Abstract

This review of the literature examines how Artificial Intelligence (AI) can be incorporated into Quality Assurance (QA) in the various sectors. It explores the application of AI methods (Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing) in increasing the defect detection, optimization of processes, and forecast quality control. The review establishes important trends, applications, advantages and issues relating to AI-driven QA systems through a thorough review of the latest research works. The results indicate that AI can contribute greatly to the level of accuracy, speed, and decision-making and allows the proactive quality regulation. Nonetheless, there are problems of data quality, interpretability and implementation costs that persist. The paper is summed up with some insights into the emerging technologies and the research directions of the future.