Reliability, safety, and sustained performance of the medical devices are highly critical issues in the contemporary healthcare system where any failure in the medical devices may have serious clinical outcomes and patient injuries. With the growing complexity and data being of a medical system, there has been the emergence of early anomaly detection in device performance data as a crucial process of preventing failure, facilitating proactive maintenance, and improving patient safety. Nevertheless, the current solutions mostly depend on conventional statistical or supervised learning schemes, which are sensitive to data imbalance, changing faults modes, and small amounts of labeled anomaly samples.Recent developments in generative deep learning, especially Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models, provide encouraging opportunities to support unsupervised and semi-supervised anomaly detection through learning the latent data distribution and detecting anomalies with respect to normal operation. Although these models continue to be adopted, they have not been systematically and comparatively reviewed in med device performance monitoring, and it is still unclear how they relate in terms of effectiveness, strength, and their ability to be deployed in safety-critical settings.This paper is a comparative critical analysis of GAN-, VAE-, and Diffusion-based architectures of early anomaly detection in medical devices performance data. To evaluate the performance of a model, we would evaluate it based on several quantitative measures such as detection accuracy, precision-recall balance, false alarm rates, detection latency and computational efficiency. The study also looks at the robustness issues in the noisy environment, scalability to high-dimensional telemetry data and concept drift resiliency, which informs about real-world applicability.The findings show that, although GANs are more efficient in restoring intricate normal samples, VAEs can offer stable latent features that can be used to score anomalies consistently, and Diffusion Models have better resilience to detect subtle and changing abnormalities. The results of these studies point to trade-offs associated with models and provide practical recommendations in the process of selecting an adequate generative frameworks in the medical safety context. Finally, this piece of work helps to develop reliable, evidence-based monitoring systems that facilitate the detection of faults early, adherence to regulations, and enhancing better clinical safety outcomes.
Generative Models for Anomaly Detection in Medical Devices, GAN-Based Anomaly Detection in Healthcare Systems, Variational Autoencoders for Medical Time-Series Monitoring, Diffusion Models for Unsupervised Anomaly Detection, Early Fault Detection in Medical Device Performance Data, Unsupervised Learning for Safety-Critical Healthcare Systems, Reliability and Predictive Maintenance of Medical Cyber-Physical Systems.
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