AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH025160032
REVIEW ARTICLE

Transforming pharmaceutical quality assurance and validation through artificial intelligence

Vaibhav Adhao1* Jaya Ambhore1* Shreyash Chaudhari1
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1 Department of Quality Assurance, Dr. Rajendra Gode College of Pharmacy, Malkapur, Maharashtra, India
Received: 17 April 2025 | Revised: 29 July 2025 | Accepted: 1 August 2025 | Published online: 13 August 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

The evolution of artificial intelligence (AI) in the pharmaceutical industry spans from its early applications in automating administrative tasks to its pivotal role in drug discovery, personalized medicine, and safety enhancement. AI contributes significantly to data analysis, real-time process monitoring, defect detection, predictive maintenance, and compliance assurance, thereby enhancing efficiency, accuracy, and regulatory adherence. This review assesses the transformative functions of AI integration in revolutionizing quality assurance and validation across the pharmaceutical industry and highlights the contribution of AI in advancing quality frameworks, core values, and smart manufacturing. Moreover, the role of AI in enhancing validation processes and the critical importance of data and algorithms are discussed. As AI continues to reshape the pharmaceutical industry, it emphasizes the synergy between technological innovation and quality enhancement.

Keywords
Artificial intelligence
Quality assurance
Validation
Pharmaceutical industry
Software development
Predictive maintenance
Compliance
Funding
None.
Conflict of interest
The authors declare they have no competing interests.
References
  1. Dasta JF. Application of artificial intelligence to pharmacy and medicine. Hosp Pharm. 1992;27:312-315.

 

  1. Duch W, Swaminathan K, Meller J. Artificial intelligence approaches for rational drug design and discovery. Curr Pharm Des. 2007;13(14):1497-1508. doi: 10.2174/138161207780765954

 

  1. Makridakis S. The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures. 2017;90:46-60.

 

  1. Saha GC, Eni LN, Saha H, et al. Artificial intelligence in pharmaceutical manufacturing: Enhancing quality control and decision making. Riv Ital Filosofia Anal Junior. 2023;14(2):116-126.

 

  1. Harrer S, Shah P, Antony B, Hu J. Artificial intelligence for clinical trial design. Trends Pharmacol Sci. 2019;40(8):577-591. doi: 10.1016/j.tips.2019.05.005

 

  1. Waqar M, Bhatti I, Khan AH. AI-powered automation: Revolutionizing industrial processes and enhancing operational efficiency. Rev Intel Artif Med. 2024;15(1):1151-1175.

 

  1. Mosisa B, Malay K, Fufa F. Transformative role of artificial intelligence in the pharmaceutical sector. J Angiother. 2024;8(9):1-7.

 

  1. Huang J, O’Connor T, Ahmed K, et al. AIChE PD2M advanced process control workshop‐moving APC forward in the pharmaceutical industry. J Adv Manuf Process. 2021;3(1):e10071.

 

  1. Cherekar R. The future of AI quality assurance: Emerging trends, challenges, and the need for automated testing frameworks. Int J Emerg Trends Comput Sci Inform Technol. 2021;2(1):19-27.

 

  1. Mohammad AS, Devidi S, Fatima N, et al. An overview of validation and basic concepts of process validation: Quality assurance view point. Asian J Pharm Technol. 2016;6(3):169-176.

 

  1. Borchert D, Zahel T, Thomassen YE, Herwig C, Suarez- Zuluaga DA. Quantitative CPP evaluation from risk assessment using integrated process modeling. Bioengineering. 2019;6(4):114. doi: 10.3390/bioengineering6040114

 

  1. Rahman SN, Katari O, Pawde DM, et al. Application of design of experiments® approach-driven artificial intelligence and machine learning for systematic optimization of reverse phase high performance liquid chromatography method to analyze simultaneously two drugs (cyclosporin A and etodolac) in solution, human plasma, nanocapsules, and emulsions. AAPS PharmSciTech. 2021;22(4):155. doi: 10.1208/s12249-021-02026-6

 

  1. Mundhra S, Kadiri SK, Tiwari P. Harnessing AI and machine learning in pharmaceutical quality assurance. J Pharm Qual Assur Qual Control. 2024;6:19-29.

 

  1. Pawar A. Recent innovations in high-performance liquid chromatography (HPLC): Method development and validation strategies. J Drug Deliv Biother. 2024;1(1):55-61.

 

  1. Gokulakrishnan D, Venkataraman S. Ensuring Data Integrity: Best Practices and Strategies in Pharmaceutical Industry. Intelligent Pharmacy. 2024. [In press].

 

  1. Samuel A. Enhancing financial fraud detection with AI and cloud-based big data analytics: Security implications. World J Adv Eng Technol Sci. 2023;9(2):417-434.

 

  1. Vaghela MC, Rathi S, Shirole RL, Verma J, Shaheen, Panigrahi S, et al. Leveraging AI and machine learning in six-sigma documentation for pharmaceutical quality assurance. Chin J Appl Physiol. 2024;40:e20240005.

 

  1. Nandhakumar D, Kumar AM, Pavithra S. Advancements in AI-powered robotic cleaning systems: Autonomous path planning, predictive maintenance, and cleanliness assessment frameworks. In: 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE; 2025. p. 1077-1082.

 

  1. Thakur A, Kumar A. Innovative technologies for the removal of pollutants in the chemical industries. In: Innovative and Hybrid Technologies for Wastewater Treatment and Recycling 2024. United States: CRC Press. p. 167-195.

 

  1. McCall J, Barnard N, Gadient K, et al. Environmental monitoring for closed robotic workcells used in aseptic processing: data to support advanced environmental monitoring strategies. AAPS PharmSciTech. 2022;23(6):215. doi: 10.1208/s12249-022-02360-3

 

  1. Popescu SM, Mansoor S, Wani OA, et al. Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management. Front Environ Sci. 2024;12:1336088.

 

  1. Sadrizadeh S. Leveraging artificial intelligence in indoor air quality management: A review of current status, opportunities, and future challenges. REHVA European HVAC J. 2024;61(1):35-37.

 

  1. Raja JR, Kella A, Narayanasamy D. The essential guide to computer system validation in the pharmaceutical industry. Cureus. 2024;16(8):e67555.

 

  1. Shekhar S. An in-depth analysis of intelligent data migration strategies from oracle relational databases to hadoop ecosystems: Opportunities and challenges. Internafional J Appl Mach Learn Computafional Intell. 2020;10(2):1-24.

 

  1. Devineni SK, Kathiriya S, Shende A. Machine learning-powered anomaly detection: Enhancing data security and integrity. J Artif Intell Cloud Comput. 2023;2:1-9.

 

  1. Baqar M, Khanda R. The Future of Software Testing: AI-Powered Test Case Generation and Validation. arXiv preprint arXiv:2409.05808; 2024.

 

  1. Ahire YS, Patil JH, Chordiya HN, Deore RA, Bairagi VA. Advanced applications of artificial intelligence in pharmacovigilance: Current trends and future perspectives. J Pharm Res. 2024;23(1):23-33.

 

  1. Wong A, Plasek JM, Montecalvo SP, Zhou L. Natural language processing and its implications for the future of medication safety: A narrative review of recent advances and challenges. Pharmacotherapy. 2018;38(8):822-841.

 

  1. Chakraborty A, Venkatraman JV. Pharmacovigilance through phased clinical trials, post-marketing surveillance and ongoing life cycle safety. In: The Quintessence of Basic and Clinical Research and Scientific Publishing. Singapore: Springer Nature Singapore; 2023. p. 427-442.

 

  1. Shukla D, Bhatt S, Gupta D, Verma S. Role of artifical intelligence in pharmacovigilance. J Drug Discov Health Sci. 2024;1(4):230-238.

 

  1. Kim HR, Sung M, Park JA, et al. Analyzing adverse drug reaction using statistical and machine learning methods: A systematic review. Medicine. 2022;101(25):e29387.

 

  1. Verma P, S. Sangle P. Role of digital transformation in inspection and certification. In: Handbook of Quality System, Accreditation and Conformity Assessment. Singapore: Springer Nature Singapore; 2023. p. 1-29.

 

  1. Bhagat D, Dorle S. The Power of Intelligent. Hyperautomation in Business and Society. Hershey: IGI Global Scientific; 2024. p. 27.

 

  1. Pathak SS, Gawai A, Biyani KR. Quality assurance in the age of personalized medicine: Challenges and opportunities. Asian J Pharm Res Dev. 2024;12(2):179-186.

 

  1. Patel P. Impact of AI on Manufacturing and Quality Assurance in Medical Device and Pharmaceuticals Industry. Int J Innovative Technol Exploring Eng. 2024;13(8):9-21.

 

  1. Krause D. Addressing the Challenges of Auditing and Testing for AI Bias: A Comparative Analysis of Regulatory Frameworks. SSRN. 2024. Available from: https://papers. ssrn.com/sol3/papers.cfm?abstract_id=5050631

 

  1. Venkata SK. AI in audit: Unlocking deep analytical-based testing. J Comput Sci Technol Stud. 2025;7(3):592-601.

 

  1. Wang C, Yang Z, Li ZS, Damian D, Lo D. Quality Assurance for Artificial Intelligence: A Study of Industrial Concerns, Challenges and Best Practices. arXiv Preprint arXiv:2402.16391; 2024.

 

  1. Halwani MA, Amirkiaee SY, Evangelopoulos N, Prybutok V. Job qualifications study for data science and big data professions. Inform Technol People. 2022;35(2):510-525.

 

  1. Shrivastava S, Patel D, Bhamidipaty A, et al. Dqa: Scalable, automated and interactive data quality advisor. In: 2019 IEEE International Conference on Big Data (Big Data). IEEE; 2019. p. 2913-2922.

 

  1. Villegas-Ch W, García-Ortiz J, Sánchez-Viteri S. Towards intelligent monitoring in IoT: AI applications for real-time analysis and prediction. IEEE Access. 2024;12:40368-40386. doi: 10.1109/ACCESS.2024.3376707

 

  1. Archana T, Stephen RK. The future of artificial intelligence in manufacturing industries. In: Industry Applications of Thrust Manufacturing: Convergence with Real-Time Data and AI; 2024. New York: IGI Global. p. 98-117.

 

  1. Bagheri M, Bagheritaba M, Alizadeh S, Parizi MS, Matoufinia P, Luo Y. AI-driven Decision-making in Healthcare Information Systems: A Comprehensive Review. Preprints; 2024.

 

  1. Elhaddad M, Hamam S. AI-driven clinical decision support systems: An ongoing pursuit of potential. Cureus. 2024;16(4):e57728.

 

  1. Atoum I, Baklizi MK, Alsmadi I, et al. Challenges of software requirements quality assurance and validation: A systematic literature review. IEEE Access. 2021;9:137613-137634.

 

  1. Pargaonkar S. Synergizing requirements engineering and quality assurance: A comprehensive exploration in software quality engineering. Int J Sci Res. 2023;12(8):2003-2007.

 

  1. Aguilar-Gallardo C, Bonora-Centelles A. integrating artificial intelligence for academic advanced therapy medicinal products: Challenges and opportunities. Appl Sci. 2024;14(3):1303.

 

  1. Harrer S, Menard J, Rivers M, et al. Artificial intelligence drives the digital transformation of pharma. In: Artificial Intelligence in Clinical Practice. United States: Academic Press; 2024. p. 345-372.

 

  1. Kulkov I. The role of artificial intelligence in business transformation: A case of pharmaceutical companies. Technol Soc. 2021;66:101629.

 

  1. Vora LK, Gholap AD, Jetha K, Thakur RR, Solanki HK, Chavda VP. Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics. 2023;15(7):1916. doi: 10.3390/pharmaceutics15071916

 

  1. Raza MA, Aziz S, Noreen M, et al. Artificial intelligence (AI) in pharmacy: An overview of innovations. Innov Pharm. 2022;13(2):1-8. doi: 10.24926/iip.v13i2.4839

 

  1. Fisher A. The future is the present: Artificial intelligence in pharmaceutical manufacturing: FDA is anticipating how AI may advance manufacturing and improve supply chain security. Pharm Technol. 2023;47(9):32-34.

 

  1. Arden NS, Fisher AC, Tyner K, Lawrence XY, Lee SL, Kopcha M. Industry 4.0 for pharmaceutical manufacturing: Preparing for the smart factories of the future. Int J Pharm. 2021;602:120554. doi: 10.1016/j.ijpharm.2021.120554

 

  1. Rosenberger S. Growth of artificial intelligence in pharma manufacturing: Lonza describes how artificial intelligence, machine learning, and big data are improving safety, quality, and sustainability-all while lowering costs. Genet Eng Biotechnol News. 2022;43(1):34-36.

 

  1. Cerchia C, Lavecchia A. New avenues in artificial-intelligence-assisted drug discovery. Drug Discov Today. 2023;28(4):103516. doi: 10.1016/j.drudis.2023.103516

 

  1. Owczarek D. The Future of Pharmaceutical Manufacturing Process: Artificial Intelligence; 2021. Available from: https://nexocode.com/blog/posts/ai-in-pharmaceutical-manufacturing/

 

  1. Parker PD, Parker C. Future of Electronic Health Records: A Challenge to Maximize their Utility; 2023. Available from: https://ssrn.com/abstract=4457214 or http://dx.doi. org/10.2139/ssrn.4457214

 

  1. Kalyane D, Sanap G, Paul D, et al. Artificial intelligence in the pharmaceutical sector: Current scene and future prospect. In: The Future of Pharmaceutical Product Development and Research. United States: Academic Press; 2020. p. 73-107.

 

  1. Chisty NM, Adusumalli HP. Applications of artificial intelligence in quality assurance and assurance of productivity. ABC J Adv Res. 2022;11(1):23-32.

 

  1. Mak KK, Wong YH, Pichika MR. Artificial intelligence in drug discovery and development. Drug Discov Eval Saf Pharmacokinetic Assays. 2024:1461-1498. doi: 10.1007/978-3-031-35529-5_92

 

  1. Blanco-Gonzalez A, Cabezon A, Seco-Gonzalez A, et al. The role of AI in drug discovery: Challenges, opportunities, and strategies. Pharmaceuticals (Basel). 2023;16(6):891.

 

  1. Suriyaamporn P, Pamornpathomkul B, Patrojanasophon P, Ngawhirunpat T, Rojanarata T, Opanasopit P. The artificial intelligence-powered new era in pharmaceutical research and development: A review. AAPS PharmSciTech. 2024;25(6):188. doi: 10.1208/s12249-024-02901-y

 

  1. Agrawal K, Nargund N. Deep learning in industry 4.0: Transforming manufacturing through data-driven innovation. In: International Conference on Distributed Computing and Intelligent Technology. Cham: Springer Nature Switzerland; 2024. p. 222-236.

 

  1. Alsaidalani R, Elmadhoun B. Quality risk management in pharmaceutical manufacturing operations: Case study for sterile product filling and final product handling stage. Sustainability. 2022;14(15):9618.

 

  1. Shi Z, Altan S, Banton D, et al. Predictive in-vitro dissolution for real-time release test (RTRT) for continuous manufacturing process on drug product. In: Continuous Pharmaceutical Processing and Process Analytical Technology. United States: CRC Press; 2023. p. 213-270.

 

  1. Mishra V, Thakur S, Patil A, Shukla A. Quality by design (QbD) approaches in current pharmaceutical set-up. Exp Opin Drug Deliv. 2018;15(8):737-758. doi: 10.1080/17425247.2018.1504768

 

  1. Galvis L, Offermans T, Bertinetto CG, et al. Retrospective quality by design r (QbD) for lactose production using historical process data and design of experiments. Comput Ind. 2022;141:103696.

 

  1. Sembiring MH, Novagusda FN. Enhancing data security resilience in AI-driven digital transformation: Exploring industry challenges and solutions through ALCOA+ principles. Acta Inform Med. 2024;32(1):65-70. doi: 10.5455/aim.2024.32.65-70

 

  1. Emeihe EV, Nwankwo EI, Ajegbile MD, Olaboye JA, Maha CC. The impact of artificial intelligence on regulatory compliance in the oil and gas industry. Int J Life Sci Res Arch. 2024;7(1):28-39.

 

  1. Tutuncuoglu BT. Beyond the productivity paradox: Unveiling the hidden role of artificial intelligence in enhancing human creativity and innovation; 2024. Available from: https://dx.doi.org/10.2139/ssrn.5246291

 

  1. Kabir M, Rana MR, Debnath A. The role of quality assurance in accelerating pharmaceutical research and development: Strategies for ensuring regulatory compliance and product integrity. J Angiother. 2024;8(12):1-1.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing