Skip to content
Research Article Open access CC BY 4.0

Leveraging AI for Enhanced Quality Assurance in Medical Device Manufacturing

Tushar Khinvasara, Stephanie Ness, Abhishek Shankar

Asian Journal of Research in Computer Science · pp. 13–35 · Published 8 Apr 2024

10.9734/ajrcos/2024/v17i6454

Abstract

The medical device sector adheres to strict regulatory frameworks, requiring precise adherence to quality assurance (QA) processes during the production process. Conventional quality assurance (QA) approaches, although successful, sometimes require substantial time and resource allocations, resulting in possible obstacles and higher expenses. The emergence of Artificial Intelligence (AI) in recent years has completely transformed quality assurance (QA) methods in different sectors, providing unparalleled prospects for improved productivity, precision, and scalability. This research examines the possibility of using AI technologies to enhance quality assurance processes in the manufacturing of medical devices. Manufacturers may improve product quality and streamline production workflows by utilising AI techniques like machine learning, computer vision, and natural language processing to automate and optimize important QA procedures. Artificial intelligence systems can analyse large amounts of data to find abnormalities, uncover flaws, and anticipate any problems in real-time. This allows for proactive intervention and reduces the chances of non-compliance hazards. In addition, AI-powered QA systems provide adaptive learning capabilities, constantly enhancing performance through feedback and adapting to changing regulatory needs. The incorporation of artificial intelligence (AI) into current quality management systems enables smooth and efficient sharing of data and compatibility, promoting a comprehensive approach to quality control throughout the whole production process.

Quality enhanced AI quality assurance artificial intelligence

Cited by 31

Artificial intelligence in quality control and product development

Ezgi Uzel Aydinocak · Artificial Intelligence in Chemical Engineering · 2026

A Literature Review on Current Application of Artificial Intelligence Technology in Medical Devices Manufacturing

Bayu Suryo Putro, Evans Afriant Nugrah, Galuh Maulana · Multidiscience : Journal of Multidisciplinary Science · 2025

Competence gap analysis of early-career Quality Engineers in the field of Quality 4.0

Andrea Sütőová, David Vykydal, Slavomira Vargova · The TQM Journal · 2025

Fetal Precision Medicine: Ai-Driven Approaches for Early Detection of Congenital Anomalies

Haitham Ahmed Najim, Lubna Abdul Kareem Habib, Farah Mohammed Habeeb Barakat · European Journal of Medical and Health Research · 2025

Integrating quality 5.0 approaches for the future of quality management

Vimal Kumar, Arpit Singh, Nagendra Sharma · The TQM Journal · 2026

AI/ML in Healthcare Manufacturing: Ensuring PPE Supply Chain Resilience

Natarajan Ravikumar · European Modern Studies Journal · 2025

Surface seal image dataset of sterile barrier packaging

Julio Zanon Diaz, Peter Corcoran · Data in Brief · 2024

Empowering Predictive Maintenance of Medical Equipment Through AI-Driven Condition Monitoring

Aminatul Saadiah Abdul Jamil, Azira Khalil, Mardhiyati Mohd Yunus · Series in BioEngineering · 2024

Showing 20 of 31 known citations — external sources report more than can currently be individually listed.

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

31

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.