<![CDATA[The transition of medical technology from passive monitoring to autonomous, closed-loop intervention is critically impeded by the latency and power inefficiencies of traditional Von Neumann computing architectures. This study investigates the efficacy of neuromorphic hardware as a solution, aiming to validate a bio-inspired architecture capable of sub-millisecond decision-making for life-critical applications. Employing a rigorous hardware-in-the-loop simulation framework, we benchmarked a custom Spiking Neural Network (SNN) against industry-standard microcontrollers, utilizing large-scale cardiac and neurological datasets to evaluate inference speed, energy consumption, and signal fidelity. Quantitative results reveal that the neuromorphic system achieved a 94% reduction in end-to-end latency and a thirty-eight-fold improvement in energy efficiency compared to the digital baseline. The event-driven architecture successfully maintained 96.4% diagnostic accuracy while operating within a negligible thermal envelope suitable for implantation. These findings definitively establish that mimicking biological asynchronous processing eliminates fatal temporal delays, validating neuromorphic “artificial brains” as the essential technological foundation for the next generation of responsive, privacy-secure, and energy-autonomous medical implants.]]>
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