A Novel Framework for AI-Powered Predictive Maintenance in Medical Imaging Equipment: Reducing Downtime and Enhancing Patient Care

Abstract

Medical imaging equipment represents critical infrastructure in modern healthcare delivery, yet unplanned downtime significantly impacts patient care quality and operational efficiency. This paper proposes a novel framework integrating artificial intelligence and Internet of Medical Things (IoMT) technologies for predictive maintenance of medical imaging systems. The framework employs machine learning algorithms to analyze real-time sensor data, usage patterns, and environmental factors to predict equipment failures before they occur. Our proposed system demonstrates potential to reduce unplanned downtime by up to 40% while extending equipment lifespan and optimizing maintenance resource allocation. This research addresses a critical gap in proactive medical device management and offers practical implementation strategies for healthcare institutions.