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ACT uncovers that only 97 observations drive the predictions for 221 clinical phenotypes, revealing potential shortcuts in medical imaging interpretations.
LLM agents can leap from 40% to 88% accuracy in complex clinical tasks simply by validating new skills against a regression budget, proving that *how* you learn matters more than *what* you learn.
A retrieval-augmented generation system that grounds LLM responses in authoritative guideline content that significantly outperformed the standalone LLM in all metrics and suggest that grounded RAG systems have significant potential to enhance clinical guideline accessibility but require further validation before clinical deployment.
