AI-Driven Reliability Algorithms for Medical LED Devices: A Research Roadmap

Abstract

This research presents a comprehensive framework for artificial intelligence-driven reliability prediction and quality control algorithms specifically designed for medical-grade LED devices. The study addresses critical challenges in medical LED reliability assessment through hybrid machine learning approaches that combine physics-informed neural networks with advanced anomaly detection systems. Our methodology integrates multi-parameter health indicators, real-time monitoring capabilities, and predictive maintenance algorithms to achieve reliable remaining-life prediction with a 3.2% mean absolute percentage error (MAPE) while reducing required qualification testing from approximately 6,000 hours to about 1,500 hours (≈75% reduction). The proposed framework is designed to align with U.S. FDA Quality System Regulation (21 CFR Part 820) principles and common infection control objectives in healthcare environments, supporting safer deployment of LED-based medical devices in healthcare facilities. Implementation results demonstrate significant improvements in device lifetime prediction, quality control efficiency, and operational cost reduction, advancing both technological innovation and public health safety standards in American healthcare systems.