Abbas Izadi - Deputy of Treatment, Mashhad University of Medical Sciences, Mashhad, Iran
Mohamad Amin Bakhshali - Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
Hadi Ghasemifard - Deputy of Treatment, Mashhad University of Medical Sciences, Mashhad, Iran
Omid Sarrafzadeh - Deputy of Treatment, Mashhad University of Medical Sciences, Mashhad, Iran

Introduction:This paper proposes an intelligent system for managing medical equipment maintenance in healthcare facilities. The system utilizes machine learning algorithms and data analytics to predict equipment failures, schedule maintenance tasks, and manage spare parts inventory efficiently. The aim is to improve equipment availability and reliability, reduce maintenance costs, and increase patient safety.Materials and Methods: The proposed system consists of several modules: data collection, preprocessing, equipment failure prediction, maintenance scheduling, spare parts inventory management, and integration. Real-world data is used to evaluate and compare the system's performance with other maintenance management approaches. Results: The results demonstrate that the proposed system can accurately predict equipment failures, schedule maintenance tasks efficiently, and manage spare parts inventory effectively. This improves equipment availability and reliability, reduces maintenance costs, and ensures that spare parts are available when needed without incurring excessive inventory costs.Conclusion:Overall, the proposed intelligent system for managing medical equipment maintenance is an effective solution for healthcare facilities to optimize maintenance operations, reduce costs, and ensure patient safety.

Medical E۱quipment, Maintenance Management, Intelligent System, Machine Learning