33 citations · 50 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks
Suhana Bedi, Hejie Cui, Miguel Fuentes +78
While large language models (LLMs) achieve near-perfect scores on medical licensing exams, these evaluations inadequately reflect the complexity and diversity of real-world clinica…
Distilling Large Language Models for Efficient Clinical Information Extraction
Karthik S. Vedula, Annika Gupta, Akshay Swaminathan +3
Large language models (LLMs) excel at clinical information extraction but their computational demands limit practical deployment. Knowledge distillation--the process of transferrin…
FactEHR: A Dataset for Evaluating Factuality in Clinical Notes Using LLMs
Monica Munnangi, Akshay Swaminathan, Jason Alan Fries +8
Verifying and attributing factual claims is essential for the safe and effective use of large language models (LLMs) in healthcare. A core component of factuality evaluation is fac…