Technologies· 2026Q1· Review
Maintenance Strategies for Medical Equipment: A Systematic Review of Their Effects on Reliability, Availability, Safety, and Costs
- 0citations
- Q1SCImago
- 2026year
Short summary
A systematic review of 74 studies reveals that while advanced technologies like IoT and machine learning are increasingly used in medical equipment maintenance, there's a significant gap between model performance and real-world improvements in reliability, availability, and cost.
AI-generated from the title and abstract; the full text is not read.
Key points
- Seventy-four studies were systematically reviewed to characterize medical equipment maintenance strategies.
- Diagnostic imaging, life support, and infusion therapy equipment were the most studied categories.
- Preventive combined with corrective maintenance was the most frequent hybrid strategy, followed by predictive maintenance.
- Advanced technologies like IoT and machine learning are increasingly applied in maintenance.
- A significant gap exists between reported model performance (e.g., predictive accuracy) and demonstrated improvements in real-world maintenance indicators (MTBF, downtime, cost).
AI-generated from the title and abstract; the full text is not read.
Abstract
Medical equipment is essential for patient diagnosis and treatment; however, its maintenance strategies remain heterogeneous. This systematic review aimed to characterize the maintenance strategies applied to medical equipment and analyze their effects on reliability, availability, safety, and costs. Following PRISMA 2020 guidelines, original studies were searched in Scopus, ScienceDirect, PubMed, and Web of Science, published between January 2015 and July 2026. Studies were included if they applied maintenance strategies (preventive, corrective, predictive, condition-based, risk-based, or reliability-centered) and evaluated their effects on reliability, availability, safety, or costs. Seventy-four studies were selected. Eleven equipment categories were identified, with diagnostic imaging, life support, and infusion therapy being the most studied. Hybrid strategies—particularly preventive combined with corrective—were the most frequent (14 studies), followed by predictive maintenance (13 studies). Advanced analytics, IoT, and machine learning were the most applied technologies. A gap persists between model performance metrics and real maintenance indicators: several studies reported high predictive accuracy, but limited evidence of improvement in MTBF, downtime, availability, or cost. This review offers an integrative framework for healthcare technology managers and identifies priorities for future research.
The authors' abstract, as published at the source. Technologies, 2026 · DOI ↗
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