Empowering Predictive Maintenance of Medical Equipment Through AI-Driven Condition Monitoring

This chapter explores the evolution of maintenance strategies for medical equipment in healthcare, focusing on the transition from reactive approaches to predictive maintenance (PdM) driven by Artificial Intelligence (AI). Conventional maintenance methods often resulted in inefficiencies, prolonged downtimes, and potential equipment damage. PdM, utilizing data analytics and machine learning, enables real-time monitoring and failure prediction, reducing unnecessary maintenance and enhancing operational efficiency. The study presented in this chapter employs a qualitative research design, combining a systematic literature review with the analysis of case studies from the literature and case reports from undergraduate interns and supervisors working in the Biomedical Engineering departments of four Malaysian hospitals. The research highlights the empowerment of PdM’s effectiveness through AI-driven condition monitoring, demonstrating outcomes such as a 20% reduction in MRI machine downtime and significant improvements in maintenance workflows through mobile app remote monitoring. By addressing the limitations of conventional preventive maintenance, AI-driven PdM not only extends the lifespan of critical medical devices but also optimizes healthcare delivery. The chapter underscores AI's transformative potential in predictive maintenance, providing a pathway toward more efficient, reliable, and cost-effective healthcare systems.