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Published August 30, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

Impact of AI on Manufacturing and Quality Assurance in Medical Device and Pharmaceuticals Industry

Creators

  • 1. Department of Quality Engineer, Medical Device Industry, USA.

Description

Abstract: Global health and well-being largely depend on the pharmaceutical and medical device industries. Manufacturing and quality assurance (QA) processes are crucial to maintaining product efficacy, safety, and regulatory compliance in these sectors. Artificial intelligence (AI) integration presents ground-breaking opportunities to enhance these processes. This study aims to systematically assess the impact of AI on manufacturing and QA in these pharmaceutical and medical device industries. It examines the benefits, challenges, and ethical and legal implications of integrating AI. It offers a thorough understanding of how AI technology can and has been successfully integrated to enhance business operations. An extensive literature analysis was carried out to investigate AI's application, role, benefits, and challenges in manufacturing and quality assurance processes in both industries. Research was also conducted on emerging trends, future developments, and regulatory issues. Increased productivity, early detection of defects, safer and higher-quality goods, improved regulatory compliance, reduced costs, and more flexibility and scalability are some advantages of AI technologies. However, significant obstacles are also to overcome, such as high capital costs, data quality and availability issues, legacy system integration, ethical concerns about bias and data privacy, difficulties with regulatory compliance, and a lack of AI-skilled workers. Case studies show how AI has been utilized to guarantee regulatory compliance and optimize processes. AI integration has much to offer the pharmaceutical and medical device industries in terms of improved manufacturing and quality assurance procedures. By addressing restrictions and seizing novel opportunities, these industries can use AI's transformative potential to support innovation, enhance product quality and safety, ensure regulatory compliance, and improve global health outcomes.

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Additional details

Identifiers

DOI
10.35940/ijitee.I9949.13090824
EISSN
2278-3075

Dates

Accepted
2024-08-15
Manuscript received on 13 July 2024 | Revised Manuscript received on 23 July 2024 | Manuscript Accepted on 15 August 2024 | Manuscript published on 30 August 2024.

References

  • J. McCarthy, M. L. Minsky, N. Rochester, and C. E. Shannon, "A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955," AI Magazine, vol. 27, no. 4, pp. 12-12, 2006.
  • K. Frankish and W. M. Ramsey, Eds., The Cambridge Handbook of Artificial Intelligence. Cambridge University Press, 2014. https://doi.org/10.1017/CBO9781139046855
  • Mordor Intelligence, "Medical Devices Market Size & Share Analysis - Growth Trends & Forecasts (2024 - 2029)," 2024. [Online]. Available: https://www.mordorintelligence.com/industry-reports/global-medical-device-technologies-market-industry
  • R. McCabe et al., "Adapting hospital capacity to meet changing demands during the COVID-19 pandemic," BMC Medicine, vol. 18, pp. 1-12, 2020. https://doi.org/10.1186/s12916-020-01781-w
  • M. Mikulic, "Global pharmaceutical industry - statistics & facts," 2024. [Online]. Available: https://www.statista.com/topics/1764/global-pharmaceutical-industry/#topicOverview
  • D. Lewis, "Pharma Industry Outlook: The Challenges and Opportunities," 2022. [Online]. Available: https://www.dcatvci.org/features/pharma-industry-outlook-the-challenges-and-opportunities/
  • U.S. Department of Commerce and International Trade Administration, "An Overview of the U.S. Medical Devices and Biopharmaceutical Industries," 2022.
  • Z. Huang, Y. Shen, J. Li, M. Fey, and C. Brecher, "A survey on AI-driven digital twins in industry 4.0: Smart manufacturing and advanced robotics," Sensors, vol. 21, no. 19, p. 6340, 2021. https://doi.org/10.3390/s21196340
  • B. M. Henrique, V. A. Sobreiro, and H. Kimura, "Literature review: Machine learning techniques applied to financial market prediction," Expert Systems with Applications, vol. 124, pp. 226-251, 2019. https://doi.org/10.1016/j.eswa.2019.01.012
  • S. S. Das et al., "AI Applications in Personalized Marketing and Customer Engagement in the Retail Banking Industry," Academy of Marketing Studies Journal, vol. 28, no. 2, 2024.
  • A. Yaseen, "Reducing industrial risk with AI and automation," International Journal of Intelligent Automation and Computing, vol. 4, no. 1, pp. 60-80, 2021.
  • Medical Device Network, "Artificial intelligence in the medical device industry: analyzing innovation, investment and hiring trends," 2024. [Online]. Available: https://www.medicaldevice-network.com/data-insights/artificial-intelligence-in-medical/?cf-view&cf-closed
  • SAP India, "Role of AI in The Pharmaceutical Industry," 2022. [Online]. Available: https://news.sap.com/india/2022/07/role-of-ai-in-the-pharmaceutical-industry
  • Pharmaceutical Technology, "Artificial intelligence in the pharmaceutical industry: analyzing innovation, investment and hiring trends," 2024. [Online]. Available: https://www.pharmaceutical-technology.com/data-insights/artificial-intelligence-in-pharma/?cf-view
  • M. M. Mariani, I. Machado, V. Magrelli, and Y. K. Dwivedi, "Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions," Technovation, vol. 122, p. 102623, 2023. https://doi.org/10.1016/j.technovation.2022.102623
  • R. Beckers, Z. Kwade, and F. Zanca, "The EU medical device regulation: Implications for artificial intelligence-based medical device software in medical physics," Physica Medica, vol. 83, pp. 1-8, 2021. https://doi.org/10.1016/j.ejmp.2021.02.011
  • T. Khinvasara, S. Ness, and A. Shankar, "Leveraging AI for Enhanced Quality Assurance in Medical Device Manufacturing," Asian Journal of Research in Computer Science, vol. 17, no. 6, pp. 13-35, 2024. https://doi.org/10.9734/ajrcos/2024/v17i6454
  • R. Roy and A. Srivastava, "Role of Artificial Intelligence (AI) in Enhancing Operational Efficiency in Manufacturing Medical Devices," The Journal of Multidisciplinary Research, pp. 35-40, 2024. https://doi.org/10.37022/tjmdr.v4i1.580
  • M. Javaid, A. Haleem, R. P. Singh, and R. Suman, "Artificial intelligence applications for industry 4.0: A literature-based study," Journal of Industrial Integration and Management, vol. 7, no. 1, pp. 83-111, 2022. https://doi.org/10.1142/S2424862221300040
  • A. Doctor, "Manufacturing of medical devices using artificial intelligence-based troubleshooters," in Biomedical Signal and Image Processing with Artificial Intelligence, Cham: Springer International Publishing, 2023, pp. 195-206. https://doi.org/10.1007/978-3-031-15816-2_11
  • T. S. Ilangakoon, S. K. Weerabahu, P. Samaranayake, and R. Wickramarachchi, "Adoption of Industry 4.0 and lean concepts in hospitals for healthcare operational performance improvement," International Journal of Productivity and Performance Management, vol. 71, no. 6, pp. 2188-2213, 2022. https://doi.org/10.1108/IJPPM-12-2020-0654
  • H. Ding, R. X. Gao, A. J. Isaksson, R. G. Landers, T. Parisini, and Y. Yuan, "State of AI-based monitoring in smart manufacturing and introduction to focused section," IEEE/ASME Transactions on Mechatronics, vol. 25, no. 5, pp. 2143-2154, 2020. https://doi.org/10.1109/TMECH.2020.3022983
  • D. M. Dave, "Revolutionizing Medical Device Implants: Unleashing the Power of Industry 5.0," International Journal of Computer Trends and Technology, vol. 71, no. 10, pp. 1-11, 2023. https://doi.org/10.14445/22312803/IJCTT-V71I10P101
  • M. A. Sujan, S. White, I. Habli, and N. Reynolds, "Stakeholder perceptions of the safety and assurance of artificial intelligence in healthcare," Safety Science, vol. 155, p. 105870, 2022. https://doi.org/10.1016/j.ssci.2022.105870
  • H. Padmanaban, "Revolutionizing Regulatory Reporting through AI/ML: Approaches for Enhanced Compliance and Efficiency," Journal of Artificial Intelligence General Science (JAIGS), vol. 2, no. 1, pp. 71-90, 2024. https://doi.org/10.60087/jaigs.v2i1.98
  • J. Bai et al., "A Comprehensive Survey on Machine Learning Driven Material Defect Detection: Challenges, Solutions, and Future Prospects," arXiv preprint arXiv:2406.07880, 2024.
  • Y. Balagurunathan, R. Mitchell, and I. El Naqa, "Requirements and reliability of AI in the medical context," Physica Medica, vol. 83, pp. 72-78, 2021. https://doi.org/10.1016/j.ejmp.2021.02.024
  • R. McDonald, "Complaint Processing Enhanced by AI in Medical Device Manufacturing," 2024. [Online]. Available: https://usdm.com/resources/case-studies/complaint-processing-enhanced-by-ai-in-medical-device-manufacturing
  • G. C. Saha et al., "Artificial Intelligence in Pharmaceutical Manufacturing: Enhancing Quality Control and Decision Making," Rivista Italiana di Filosofia Analitica Junior, vol. 14, no. 2, 2023.
  • S. K. Bhattamisra et al., "Artificial intelligence in pharmaceutical and healthcare research," Big Data and Cognitive Computing, vol. 7, no. 1, p. 10, 2023. https://doi.org/10.3390/bdcc7010010
  • L. K. Vora et al., "Artificial intelligence in pharmaceutical technology and drug delivery design," Pharmaceutics, vol. 15, no. 7, p. 1916, 2023. https://doi.org/10.3390/pharmaceutics15071916
  • M. Stasevych and V. Zvarych, "Innovative robotic technologies and artificial intelligence in pharmacy and medicine: paving the way for the future of health care—a review," Big Data and Cognitive Computing, vol. 7, no. 3, p. 147, 2023. https://doi.org/10.3390/bdcc7030147
  • R. Ranebennur et al., "Development of Automated Quality Assurance Systems for Pharmaceutical Manufacturing: A Review," Journal of Coastal Life Medicine, vol. 11, pp. 1855-1864, 2023.
  • R. Thakur, "Quality Control and Assurance in Pharmaceutical Manufacturing Processes," International Journal of Transcontinental Discoveries, vol. 4, no. 1, pp. 27-32, 2017.
  • N. S. Arden et al., "Industry 4.0 for pharmaceutical manufacturing: Preparing for the smart factories of the future," International Journal of Pharmaceutics, vol. 602, p. 120554, 2021. https://doi.org/10.1016/j.ijpharm.2021.120554
  • R. S. Patil, S. B. Kulkarni, and V. L. Gaikwad, "Artificial intelligence in pharmaceutical regulatory affairs," Drug Discovery Today, p. 103700, 2023. https://doi.org/10.1016/j.drudis.2023.103700
  • M. Tshehla-Nkuna et al., "Exploring the Impact of Advanced Manufacturing Technologies in South Africa's Pharmaceutical Industry," in 2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA), IEEE, 2024, pp. 1-6. https://doi.org/10.1109/ACDSA59508.2024.10467315
  • GlobalData, "Applications of artificial intelligence in the pharmaceutical industry," 2024. [Online]. Available: https://pharma.nridigital.com/pharma_aug23/case-studies-artificial-intelligence-pharmaceutical-industry https://doi.org/10.3390/app14031303
  • C. Aguilar-Gallardo and A. Bonora-Centelles, "Integrating Artificial Intelligence for Academic Advanced Therapy Medicinal Products: Challenges and Opportunities," Applied Sciences, vol. 14, no. 3, p. 1303, 2024.
  • S. R. Tummala and N. Gorrepati, "AI-driven Predictive Analytics for Drug Stability Studies," Journal of Pharma Insights and Research, vol. 2, no. 2, pp. 188-198, 2024.
  • Y. W. Park and J. Shintaku, "Sustainable Human–Machine Collaborations in Digital Transformation Technologies Adoption: A Comparative Case Study of Japan and Germany," Sustainability, vol. 14, no. 17, p. 10583, 2022. https://doi.org/10.3390/su141710583
  • Katiyar, R., Gangwar, Dr. M., & Singh, V. K. (2022). A Review on Quality Assurance in Mobile Ad Hoc Network and Applications. In International Journal of Innovative Technology and Exploring Engineering (Vol. 11, Issue 9, pp. 40–43). https://doi.org/10.35940/ijitee.g9219.0811922
  • Sukmana, F., & Rozi, F. (2019). Software Design and Development for Optimizing Quality Assurance Assessments. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 3, pp. 3384–3389). https://doi.org/10.35940/ijrte.c5029.098319
  • Venkatesh, Dr. A. N. (2019). Reimagining the Future of Healthcare Industry through Internet of Medical Things (IoMT), Artificial Intelligence (AI), Machine Learning (ML), Big Data, Mobile Apps and Advanced Sensors. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1, pp. 3014–3019). https://doi.org/10.35940/ijeat.a1412.109119