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Self-supervised learning outperforms clinical supervision in aligning medical image representations, challenging assumptions about model interchangeability.
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.
