AI-Powered Failure Mode and Effects Analysis (FMEA) for Cardiovascular Devices: A Modern Framework for Proactive Risk Management

Aniruddha Dhole

Authors

Aniruddha Dhole Design Quality Assurance , USA

Keywords:

AI, FMEA, Cardiovascular Devices, Risk Management, Predictive Analytics, NLP, Medical Device Reliability, Failure Modes

Abstract

Stents and implantable defibrillators are examples of cardiovascular equipment that keep people alive. The safety and dependability of these devices are very important. The Conventional Failure Mode and Effects Analysis (FMEA) methods are well recognized, and they tend to be subjective, reactive, and highly dependent on the past and human knowledge. This paper explores how the Failure Mode and Effects Analysis (FMEA) process of cardiovascular devices can be enhanced with the help of Artificial Intelligence (AI), namely, natural language processing (NLP), machine learning (ML), and predictive analytics. We suggest a modern FMEA framework that uses AI to accurately find possible failure modes and automatically update risk profiles using real-time data from clinical trials, manufacturing, and post-market surveillance. The objective is to demonstrate that AI-enhanced FMEA can transform device design and manufacturing into a more proactive, data-informed safety framework.

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