AI Horizons in Healthcare: Deep Transfer Learning–Enhanced Quality Assurance and Maintenance for Data-Scarce, Cyber-Threatened Environments

Abstract

Healthcare systems in many regions face a dual challenge: sparse, heterogenous clinical data and an escalating cyber threat landscape that undermines availability and trust. This paper proposes a practical, deployable framework that combines deep transfer learning for quality assurance (QA) with an integrated maintenance system (IMS) to modernize healthcare delivery in such constrained environments. The deep transfer learning component leverages pre-trained models from related domains (medical imaging, physiological signal analysis, and electronic health record patterns) and adapts them via fine-tuning and domain adaptation techniques to perform QA tasks — including anomaly detection, data completeness checks, semantic validation, and provenance inference — under severe data scarcity. The IMS couples predictive maintenance for edge devices, models, and software stacks with continuous security posture assessment, automated patch orchestration, and resilient fallback strategies. We evaluate the framework conceptually using representative case studies (rural diagnostic centers, small clinics with legacy devices, and emergency response networks) and present a mixed-methods results-and-discussion that synthesizes expected improvements in diagnostic consistency, model robustness, and operational uptime. The approach emphasizes privacy-preserving transfer, lightweight model footprints, and security-by-design, enabling accelerated modernization with minimal disruption and risk.