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DOI: HARNESSING ARTIFICIAL INTELLIGENCE FOR REAL-TIME QUALITY ASSURANCE IN MEDICAL DEVICE MANUFACTURING
Phani Chandra Barla , Principal Quality Engineer, Senseonics Inc 20451 Seneca Meadows Parkway, Germantown, MD 20876-7005 Dr. Laina Karthikeyan , Professor, Department of Biological Sciences, New York City College of Technology, 285 Jay St, Brooklyn, NY 11201Abstract
The production process for medical devices must precisely follow quality assurance (QA) procedures to comply with the sector's stringent regulatory requirements. Although conventional QA procedures are generally effective, they can be time-consuming and resource-intensive, which can lead to problems and increased costs. With its unprecedented potential for increased productivity, accuracy, and scalability, Artificial Intelligence (AI) has revolutionized quality assurance (QA) approaches across industries since its inception. In this study, we look at how artificial intelligence (AI) could improve medical device quality assurance procedures. Artificial intelligence (AI) methods such as computer vision, machine learning, and natural language processing can automate and optimize critical QA operations, allowing manufacturers to expedite production workflows, while improving product quality. Systems powered by artificial intelligence can sift through mountains of data in search of irregularities, defects, and faults, and they can do it in real-time. This lessens the likelihood of non-compliance problems and enables proactive response. Furthermore, QA systems driven by AI offer the ability to learn and adapt, which allows them to continuously improve performance by analyzing input and meeting evolving regulatory requirements.
Keywords
Quality Assurance, Medical Device Manufacturing, Artificial Intelligence
References
Harrer S, et al. Artificial intelligence for clinical trial design. Trends Pharmacol. Sci. 2019; 40:577–591. doi: 10.1016/j.tips. 2019.05.005. [PubMed] [CrossRef] [Google Scholar].
Li BT, et al. Reimagining patient-centric cancer clinical trials: a multi-stakeholder international coalition. Nat. Med. 2022; 28:620–626. doi: 10.1038/s41591-022-01775-6. [PubMed] [CrossRef] [Google Scholar]
Dagenais S, et al. Use of real-world evidence to drive drug development strategy and inform clinical trial design. Clin. Pharmacol. Ther. 2022; 111:77–89. doi: 10.1002/ cpt.2480. [PMC free article]
Liu R, et al. Evaluating eligibility criteria of oncology trials using real-world data and AI. Nature. 2021; 592:629–633.
Ithapu VK, et al. Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment. Alzheimers Dement. 2015; 11:1489–1499. doi: 10.1016/j.jalz. 2015.01.10. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Ezzati A, et al. Machine learning predictive models can improve efficacy of clinical trials for Alzheimer’s disease. J. Alzheimers Dis. 2020; 74:55–63. doi: 10.3233/JAD-190822. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Mohan A, et al. A machine-learning derived Huntington’s disease progression model: insights for clinical trial design. Mov.
de Jong J, et al. Towards realizing the vision of precision medicine: AI based prediction of clinical drug response. Brain. 2021; 144:1738–1750. doi: 10.1093/brain/awab-108. [PMC free article] [PubMed] [CrossRef] [Google Scholar].
Hassanzadeh H, et al. Matching patients to clinical trials using semantically enriched.
Alexander M, et al. Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients. JAMIA.
Haddad T, et al. Accuracy of an artificial intelligence system for cancer clinical trial eligibility screening: retrospective Pilot Study. JMIR Med. Inform. 2021;9:e27767.
Beck JT, et al. Artificial intelligence tool for optimizing eligibility screening for clinical trials in a large community cancer center. JCO Clin. Cancer Inform. 2020; 4:50–59. doi: 10.1200/CCI.19.00079. [PubMed] [CrossRef] [Google Scholar]
Kim J, et al. Review of the performance metrics for natural language systems for clinical trials matching. Stud. Health Technol. Inform. 2022; 290:641–644. [PubMed] [Google Scholar]
Unlearn works with pharma sponsors, biotech companies, and academic institutions. https://www.businesswire .com/news/home/20220419005354/en
European Medicines Agency releases for public consultation its draft policy on the publication and access to clinical-trial.
Abramson A, et al. A flexible electronic strain sensor for the real-time monitoring of tumor regression. Sci. Adv. 2022; 8:eabn6550. doi: 10.1126/sciadv.abn6550. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Warnat-Herresthal S, et al. Swarm learning for decentralized and confidential clinical machine learning. Nature. 2021;594:265–270. doi: 10.1038/s41586-021-03583-3. [PMC free article] [PubMed] [CrossRef] [Google Scholar
18. Lipson, S.M., Gordon, R.E., Ozen, F.S., Karthikeyan, L., Stotzky, G. Effect of cranberry and grape juices on tightjunction function and structural integrity among rotavirus-infected money kidney epithelial cell culture monolayers. Food Environ. Virol. 3, 2011; 46-54.
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Copyright (c) 2024 Phani Chandra Barla, Dr. Laina Karthikeyan
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