AI-Based Predictive Maintenance in Medical Equipment

Abstract

Unplanned medical equipment failures disrupt clinical workflows, delay patient care, and impose substantial financial costs -- a single MRI scanner failure during peak scheduling can cancel 12-18 patient examinations and cost USD 15,000-30,000 in lost revenue and emergency repair charges. Predictive maintenance (PdM) leverages sensor data and machine learning to forecast equipment failures before they occur, enabling proactive servicing that minimises downtime while avoiding the waste of excessive scheduled maintenance. This study presents the AI-Based Predictive Maintenance for Medical Equipment Framework (AIPMEF), evaluating five PdM approaches -- threshold-based condition monitoring, statistical degradation modelling, supervised failure classification, deep learning anomaly detection, and digital twin simulation -- across four equipment categories: diagnostic imaging systems (MRI, CT, X-ray), laboratory analysers, patient monitoring networks, and surgical robotic platforms. Our Predictive Maintenance Performance Score (PMPS) integrates failure prediction accuracy, lead time adequacy, false alarm rate, maintenance cost reduction, and clinical workflow preservation. Deep learning anomaly detection achieved the highest PMPS (0.918) through convolutional autoencoders that learn normal operational signatures from 14.6 million sensor readings and detect deviation patterns 8.4 days before failure onset, while digital twin simulation achieved the highest lead time (12.6 days) through physics-informed models that project component degradation trajectories from current operating conditions.