Enhancing clinicians’ trust in large language models via transparent source attribution: A randomized controlled evaluation in uro-oncology

Abstract

Our findings show that combining LLMs with RAG with in-line references and source text previews markedly enhances perceived source attribution and verifiability compared to state-of-the-art conventional LLMs. Importantly, this approach is readily transferable across medical subspecialties, enabling reliable and up-to-date clinical decision support.

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