The rapid convergence of artificial intelligence, Internet of Things infrastructures, and healthcare digitalization has placed unprecedented demands on hardware systems that are not only computationally powerful but also resilient, energy efficient, and diagnostically autonomous. Contemporary healthcare automation relies increasingly on distributed edge devices, embedded inference accelerators, and smart sensor networks, yet these technological ecosystems operate within global supply chains that are fragmented, geopolitically unstable, and vulnerable to disruptions in semiconductor manufacturing, logistics, and component sourcing. Within this context, the sustainability and reliability of AI-enabled healthcare infrastructure can no longer be conceptualized purely as a software or algorithmic problem; it must be understood as a deeply hardware-bound challenge shaped by supply chain resilience, diagnostic automation, and architectural adaptability. Building on recent developments in resilient AI hardware diagnostics and supply chain-aware design frameworks, this article develops a comprehensive theoretical and methodological synthesis that integrates hardware acceleration, emerging memory technologies, energy-aware embedded systems, and healthcare automation requirements into a unified analytical model. Central to this framework is the notion that diagnostic automation embedded directly into AI hardware platforms enables self-monitoring, fault isolation, and adaptive reconfiguration, thereby mitigating the risks posed by component variability and supply chain instability, as demonstrated by recent work on resilient AI hardware in fragmented supply chains (Chandra et al., 2026). Through an extensive engagement with the literature on hardware acceleration for machine learning, edge AI, ultra-low power microcontrollers, and healthcare-oriented automation systems, this study articulates how hardware-level diagnostics and architectural redundancy can be aligned with the operational imperatives of smart healthcare environments. The methodological approach is qualitative and theoretical, grounded in systematic interpretation of cross-disciplinary sources, enabling the construction of a conceptual results model that maps relationships among hardware resilience, energy efficiency, diagnostic intelligence, and healthcare service continuity. The findings suggest that resilient diagnostic automation is not merely a technical add-on but a structural necessity for sustainable healthcare AI ecosystems, especially when deployed in globally distributed and resource-constrained contexts. By critically analyzing competing scholarly perspectives and identifying unresolved tensions between performance optimization and supply chain robustness, the article contributes a long-term research agenda for hardware-centric sustainability in digital healthcare.

Edge artificial intelligence, hardware resilience, diagnostic automation, healthcare IoT

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