Mausami Chandrakantbhai Vaghela^1*, Sanjesh Rathi², Rahul L. Shirole³, Jyoti Verma⁴, Shaheen⁵, Saswati Panigrahi⁶,

Shubham Singh⁷ 

¹Assistant Professor, Department of Pharmaceutical Sciences, Faculty of Health Sciences, Marwadi University, Rajkot, India

²Professor and Principal, School of Pharmacy, Rai University, Ahmedabad, Gujarat, India

³Associate Professor, Department of Pharmacology, DCS’s A.R.A. College of Pharmacy, Nagaon, Dhule (MS) India

⁴Associate Professor, School of Pharmacy, Rai University, Ahmedabad, Gujarat, India

⁵Associate Professor, Shadan Women's College of Pharmacy, Khairathabad, Hyderabad, Telangana- 500004, India

⁶Assistant Professor, St. John Institute of Pharmacy and Research, Vevoor, Manor Road, Palghar (E), District Palghar, Palghar, Maharashtra-401404, India

⁷Assistant Professor, School of Pharmacy, Rai University, Ahmedabad, Gujarat, India

* Address    for  Correspondence:

Dr. Mausami Chandrakantbhai Vaghela

Assistant Professor, Department of Pharmaceutical Sciences, Faculty of Health Sciences, Marwadi University, Rajkot, India

E-mail: mausami_2123@yahoo.com

ORCID ID: 0000-0001-6196-221X

Abstract

The pharmaceutical industry must maintain stringent quality assurance standards to ensure product safety and regulatory compliance. A key component of the well-known Six Sigma methodology for process improvement and quality control is precise and comprehensive documentation. However, there are a number of significant issues with traditional documentation procedures, including as slowness, human error, and difficulties with regulatory standards. This review research looks at innovative ways to employ machine learning (ML) and artificial intelligence (AI) to enhance Six Sigma documentation processes in the pharmaceutical sector. AI and ML provide cutting-edge technologies that have the potential to drastically alter documentation processes by automating data entry, collection, and analysis. Natural language processing (NLP) and computer vision technologies have the potential to significantly reduce human error rates and increase the efficacy of documentation processes. By applying machine learning algorithms to support real-time data analysis, predictive analytics, and proactive quality management, pharmaceutical organizations may be able to identify potential quality issues early on and take proactive efforts to address them. Combining AI and ML improves documentation accuracy and reliability while also strengthening compliance with stringent regulatory criteria. The primary barriers and limitations to the current state of Six Sigma documentation in the pharmaceutical industry are identified in this study. It examines the fundamentals of AI and ML with an emphasis on their specific applications in quality assurance and potential benefits for Six Sigma processes. The report includes extensive case studies that highlight notable developments and explain how AI/ML enhanced documentation is used in the real world.

Keywords  Traditional Documentation Challenges, AI and ML Integration, Benefits of Integration, Case Studies, Challenges and Considerations, Future Directions

PDF Leveraging AI and Machine Learning in Six-Sigma Documentation for Pharmaceutical Quality Assurance.pdf