Assoziationsregeln - Analyse eines Data Mining Verfahrens

Bollinger, Toni

doi:10.1007/s002870050036

Cited by 14 publications

(2 citation statements)

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“…The Quality Management System (QMS) solutions play a crucial role in driving the AI revolution in quality management [72]. They offer a centralized platform for the storage, analysis, and collaboration of data, enabling stakeholders to access up-to-date information from any location in the world [73]. This high level of connection is especially beneficial in industries that have several manufacturing units or worldwide supply chains [74].…”

Section: Ai and Qmsmentioning

confidence: 99%

Leveraging AI for Enhanced Quality Assurance in Medical Device Manufacturing

Khinvasara,

Ness,

Shankar

2024

AJRCoS

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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.

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