Artificial Intelligence Integration In Electronic Quality Management Systems For Life Sciences

Abstract

The pharmaceutical industry faces a growing problem. Old-fashioned quality management methods simply cannot keep up with today's massive data volumes and ever-changing regulatory demands. The present study takes a hard look at how artificial intelligence can transform electronic quality management systems in drug manufacturing. A total of 47 published studies were reviewed using well-established quality assessment tools, including the Mixed Methods Appraisal Tool and ROBINS-I framework. Two reviewers independently rated each study, achieving strong agreement with Cohen's kappa of 0.78. The analysis covers how machine learning, natural language processing, and predictive analytics are being used across the industry. The review examined deviation classification, document analysis, risk assessment, and complaint handling. The results are encouraging. AI consistently outperforms traditional rule-based approaches across nearly all quality management functions. The research also proposes something new: an Adaptive Multi-Dimensional Risk Quantification Framework. The framework functions as a smart system that pulls together risk signals from multiple sources and learns from mistakes over time. The technical backbone includes sigmoid normalization, temporal difference learning, and eligibility trace mechanisms. Testing on 2,847 real production batches showed F1 score improvements of 23-31% compared to standalone machine learning models. False alerts dropped significantly, and quality teams could focus efforts where results mattered most. That said, some troubling gaps exist in current research. Most studies do not address how to explain black-box model decisions to regulators. Few tackle the bias that can creep into training data. Long-term performance data spanning multiple years is hard to find. Such issues matter because FDA 21 CFR Part 11, ISO 13485, and ICH Q9 all have expectations that organizations understand how AI systems make decisions. The bottom line is straightforward: AI integration represents a genuine step forward for pharmaceutical quality assurance. But success requires more than just good algorithms. Organizations need solid data governance, trained staff who understand both AI and quality systems, and validation approaches that satisfy regulators. When done right, AI enables a shift from reactive firefighting to proactive quality management. That shift ultimately protects patients and ensures medicines work as intended.