7 citations · 8 across the 9 of their papers we have counts for
4 papers · 1 filter
Retrieval-Augmented Guardrails for AI-Drafted Patient-Portal Messages: Error Taxonomy Construction and Large-Scale Evaluation
Wenyuan Chen, Fateme Nateghi Haredasht, Kameron C. Black +4
Asynchronous patient-clinician messaging via EHR portals is a growing source of clinician workload, prompting interest in large language models (LLMs) to assist with draft response…
MedFactEval and MedAgentBrief: A Framework and Workflow for Generating and Evaluating Factual Clinical Summaries
François Grolleau, Emily Alsentzer, Timothy Keyes +17
Evaluating factual accuracy in Large Language Model (LLM)-generated clinical text is a critical barrier to adoption, as expert review is unscalable for the continuous quality assur…
Asking the Right Questions: Benchmarking Large Language Models in the Development of Clinical Consultation Templates
Liam G. McCoy, Fateme Nateghi Haredasht, Kanav Chopra +15
This study evaluates the capacity of large language models (LLMs) to generate structured clinical consultation templates for electronic consultation. Using 145 expert-crafted templ…
Embedding-Driven Diversity Sampling to Improve Few-Shot Synthetic Data Generation
Ivan Lopez, Fateme Nateghi Haredasht, Kaitlin Caoili +2
Accurate classification of clinical text often requires fine-tuning pre-trained language models, a process that is costly and time-consuming due to the need for high-quality data a…