4 citations · 13 across the 13 of their papers we have counts for
5 papers · 1 filter
A Multi-agent Large Language Model Framework to Automatically Assess Performance of a Clinical AI Triage Tool
Adam E. Flanders, Yifan Peng, Luciano Prevedello +6
Purpose: The purpose of this study was to determine if an ensemble of multiple LLM agents could be used collectively to provide a more reliable assessment of a pixel-based AI triag…
Enhancing Health Fact-Checking with LLM-Generated Synthetic Data
Jingze Zhang, Jiahe Qian, Yiliang Zhou +1
Fact-checking for health-related content is challenging due to the limited availability of annotated training data. In this study, we propose a synthetic data generation pipeline t…
Generative Large Language Models Trained for Detecting Errors in Radiology Reports
Cong Sun, Kurt Teichman, Yiliang Zhou +8
In this retrospective study, a dataset was constructed with two parts. The first part included 1,656 synthetic chest radiology reports generated by GPT-4 using specified prompts, w…
MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs
Juncheng Wu, Wenlong Deng, Xingxuan Li +12
Medical tasks such as diagnosis and treatment planning require precise and complex reasoning, particularly in life-critical domains. Unlike mathematical reasoning, medical reasonin…
A foundation model for human-AI collaboration in medical literature mining
Zifeng Wang, Lang Cao, Qiao Jin +20
Systematic literature review is essential for evidence-based medicine, requiring comprehensive analysis of clinical trial publications. However, the application of artificial intel…