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University of Illinois Urbana-Champaign
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A self-learning diagnostic agent that leverages dual-memory and reinforcement learning achieves state-of-the-art accuracy (+19.6%) and stable long-horizon learning, suggesting a promising path to experience-driven clinical AI.
LLMs can diagnose better by explicitly reasoning about "what if" scenarios, just like doctors do in training.