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LLMs have been characterized as stochastic parrots, probabilistic systems that merely remix text without understanding and predict the next word. But the frontier is shifting. Today, the question is no longer whether LLMs can imitate clinical expertise, but how we transform them into regulated medical devices that can interview patients, form preliminary diagnoses, triage safely, and even prescribe.
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I’ve spent the last few days in Seattle at Amazon’s internal Machine Learning Conference (AMLC). If last year was defined by the frontier of GenAI capabilities, this year the focus shifted decisively toward agents, reliability, and real-world deployment. The conversation has moved from “Can we do X?” to “How do we evaluate, govern, and safely operationalize X at scale?”. It felt like a distinctly Amazonian event: pragmatic, execution-oriented, and full of hallway discussions about shipping real systems and delivering customer impact.