ArticleJournal of multidisciplinary healthcare2025
AI-Driven Decision Support Framework for Preventing Medical Equipment Failure and Enhancing Patient Safety: A New Perspective.
Article in Journal of multidisciplinary healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Integrating Predictive Analytics into Hospital Automation: Comparative Evaluation of Prediction Models for Patient Monitor Demand in Operating Rooms.Bioengineering (Basel, Switzerland) · 2026Article
- The role of medical equipment reliability assessment in improving healthcare service quality: a scoping review.BMC health services research · 2026Article
- The relationship between professional competence and work engagement among Chinese biomedical laboratory technicians: a moderated mediation model.BMC psychology · 2026Article
- Article
- A data-driven priority assessment and deployment framework for medical equipment maintenance in a tertiary hospital.Frontiers in artificial intelligence · 2026Article
- Artificial Intelligence and the Future of Cardiac Implantable Electronic Devices: Diagnostics, Monitoring, and Therapy.Journal of clinical medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical equipment failures pose serious risks to patient safety and healthcare system efficiency. Although AI-based predictive maintenance (PdM) has shown promise in other industries, its application in healthcare remains fragmented and insufficiently aligned with human-centered principles. This perspective paper proposes a novel AI-driven decision support framework that integrates systems thinking and prioritizes human-centered design. By leveraging real-time sensor data and historical maintenance records, the framework proactively predicts equipment failures and reduces downtime. It incorporates insights from key stakeholders, including biomedical engineers, technicians, patients, and administrators, to ensure human-centered and ethically responsible implementation. The paper also addresses major challenges such as data integration, human factors, and organizational readiness, offering practical strategies for sustainable adoption. This work contributes to the evolving role of AI in healthcare by emphasizing empathy, stakeholder collaboration, and safety, ultimately promoting more reliable medical devices and improved patient outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.