Harnessing Artificial Intelligence and Machine Learning in Six-Sigma Documentation for Pharmaceutical Quality Assurance.

Abstract

Upholding strict quality assurance standards is essential to the pharmaceutical sector in order to guarantee product safety and regulatory compliance. The well-known Six Sigma approach for quality control and process improvement places a strong emphasis on accurate and thorough documentation. Traditional documentation techniques, however, often encounter serious problems, including as slowness, human error, and trouble with regulatory requirements. The creative use of artificial intelligence (AI) and machine learning (ML) to improve Six Sigma documentation procedures in the pharmaceutical industry is examined in this review study. By automating data input, collecting, and analysis, AI and ML provide cutting-edge technologies that may completely change documentation procedures. Technologies like computer vision and natural language processing (NLP) may greatly lower human mistake rates and boost the effectiveness of documentation procedures. Pharmaceutical businesses may see possible quality problems early on and take proactive measures to remedy them by using machine-learning algorithms to facilitate real-time data analysis, predictive analytics, and proactive quality management. The combination of AI and ML enhances compliance with strict regulatory standards while also improving the accuracy and dependability of paperwork. This study identifies the main obstacles and constraints to the present level of Six Sigma documentation in the pharmaceutical business. It explores the foundations of AI and ML, focusing on their particular uses in QA and their possible advantages for Six Sigma procedures. Comprehensive case studies that demonstrate the real-world use of AI/ML enhanced documentation are included in the study, showing significant advancements.