_______________________________________________________________________________________________ AI in Healthcare
AI is now reshaping diagnostics, surgical precision, emergency response, and clinical training. But as impressive as the algorithms are, they are not the full story. The success stories previously outlined are to be celebrated, but there’ s a reason they are few and far between – and it’ s not due to the limitations of AI. Healthcare is now entering a phase where AI systems are no longer isolated tools, but active collaborators embedded directly into clinical workflows. When AI is supporting a diagnosis, guiding a scalpel, or coordinating emergency care, any issues with connectivity, such as latency, become a major risk factor. In the context of healthcare, performance is defined by how fast data can move, how predictably systems can respond, and how seamlessly human expertise can be augmented by machine intelligence. The difference between a viable remote procedure and an unusable one is often not compute power or model accuracy, but whether the underlying infrastructure can deliver ultra-low latency, consistently and at scale.
From model training to medical inference
Much of the public conversation around AI in healthcare still focuses on training. We talk about larger models, richer datasets,
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