AI-Driven Healthcare Modernization in Data-Scarce Regions: A Quality-Assured Integrated Maintenance Management System with Deep Transfer Learning and Cybersecurity Threat Intelligence
DOI:
https://doi.org/10.15680/IJMRSETM.2025.0102007Keywords:
AI for low-resource healthcare, transfer learning, predictive maintenance, CMMS, quality assurance, cyber threat intelligence, biomedical equipment reliability, data-scarce ML, integrated maintenance management, LMIC digital health.Abstract
Low-resource and data-scarce regions face persistent challenges: aged/poorly maintained biomedical equipment, scarce labeled clinical data, weak cybersecurity posture, and limited local engineering capacity. This paper proposes an integrated, quality-assured maintenance management ecosystem for healthcare facilities in such regions that combines (1) a lightweight Computerized Maintenance Management System (CMMS) augmented with IoT telemetry and prioritized workflows, (2) deep transfer learning pipelines to enable high-performance predictive maintenance and clinical decision support under extreme data scarcity, and (3) an embedded cyber threat intelligence (CTI) layer to protect patient data and operational continuity. The architecture emphasizes modularity, human-centered design, and continuous quality assurance (QA) cycles to match local constraints (intermittent connectivity, low compute, limited staff). We evaluate expected benefits qualitatively and via simulated experiments on publicly available, small-sample datasets and maintenance logs (transfer learning fine-tuning experiments and RUL estimation simulations) and show improved predictive accuracy versus training-from-scratch approaches and measurable reductions in downtime risk when maintenance is prioritized with ML-driven scheduling. The system also reduces common cyber risks by integrating CTI feeds, anomaly detection, and operational playbooks. We conclude with deployment guidance, limitations, and a research roadmap for field trials and policy alignment.
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