Factory-Grade GPU Diagnostic Automation for Computer-Vision-Driven Infrastructure Health Monitoring and Radiology-Scale AI Workloads

Abstract

The accelerating convergence of artificial intelligence, large-scale computer vision, and high-performance computing has transformed both industrial infrastructure monitoring and data-intensive clinical imaging. These domains now rely on heterogeneous graphics processing unit ecosystems that must operate continuously under heavy computational and environmental stress while maintaining reliability, determinism, and reproducibility. Recent research has demonstrated that even small deviations in GPU health, thermal stability, or firmware state can propagate into subtle model degradation, unpredictable inference errors, and biased decision-making in safety-critical applications. Within this evolving context, factory-grade diagnostic automation for GPUs has emerged as a foundational technological layer that underpins the credibility of artificial intelligence pipelines, as rigorously articulated in the work of Lulla, Chandra, and Ranjan (2025). Their investigation into automated diagnostic infrastructures for GeForce and data-centre GPUs represents a critical inflection point in understanding how hardware-level introspection, telemetry, and predictive maintenance shape the epistemic trustworthiness of computational intelligence.

This article develops a comprehensive theoretical and applied framework that situates factory-grade GPU diagnostic automation at the centre of modern computer-vision-driven structural health monitoring and radiology-scale machine learning systems. Drawing on contemporary advances in crack detection, bridge inspection, surface damage analysis, and medical image interpretation, it argues that hardware reliability is no longer an invisible substrate but a co-determinant of algorithmic validity. By synthesizing research on deep learning-based defect detection, pixel-wise segmentation, UAV-enabled imaging, and large language model-assisted radiology reporting, the paper demonstrates that GPU diagnostics mediate not only performance but also fairness, reproducibility, and safety across these fields. The theoretical contribution lies in framing GPU diagnostic automation as a form of infrastructural epistemology that governs how evidence is produced, processed, and trusted in digital sensing environments.

Methodologically, the study adopts a multi-layered analytical design that integrates literature-based system modeling, comparative architectural analysis of modern GPU platforms, and conceptual mapping of diagnostic telemetry to machine learning reliability. This approach allows for the exploration of how automated fault detection, thermal profiling, memory integrity verification, and firmware consistency influence the stability of convolutional neural networks and large language models when deployed in real-world inspection and medical settings. Results are interpreted through a critical lens that connects observed diagnostic capabilities with reported improvements in surface defect recognition, bridge damage detection, and radiology report accuracy. The findings suggest that hardware-aware AI pipelines exhibit measurably higher robustness, reduced drift, and greater transparency, corroborating the necessity of integrating factory-grade diagnostics into end-to-end system design (Lulla et al., 2025).

The discussion extends these results into a broader scholarly debate concerning the invisibility of infrastructure in artificial intelligence research. It challenges the prevailing software-centric paradigm by demonstrating that GPU health monitoring constitutes a form of methodological control analogous to calibration in traditional scientific instrumentation. The article further explores ethical and regulatory implications, particularly in contexts such as post-earthquake building safety assessment and automated medical diagnosis, where erroneous outputs may carry severe societal consequences. Ultimately, this work advances the argument that factory-grade GPU diagnostic automation is not merely a technical convenience but a scientific necessity for sustaining the integrity of AI-driven knowledge production in both civil engineering and radiological practice.