21 citations · 48 across the 9 of their papers we have counts for
Showing 2024 · cs.CLShow all
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cs.CL2024
oRetrieval Augmented Generation for 10 Large Language Models and its Generalizability in Assessing Medical Fitness
Yu He Ke, Liyuan Jin, Kabilan Elangovan +10
Large Language Models (LLMs) show potential for medical applications but often lack specialized clinical knowledge. Retrieval Augmented Generation (RAG) allows customization with d…
cs.CL2024★ 19 cited
Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report
YuHe Ke, Liyuan Jin, Kabilan Elangovan +7
Purpose: Large Language Models (LLMs) hold significant promise for medical applications. Retrieval Augmented Generation (RAG) emerges as a promising approach for customizing domain…
cs.CL2024★ 5 cited
Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias
Yu He Ke, Rui Yang, Sui An Lie +4
Background: Cognitive biases in clinical decision-making significantly contribute to errors in diagnosis and suboptimal patient outcomes. Addressing these biases presents a formida…