10 citations · 14 across the 4 of their papers we have counts for
4 papers
Monitoring Deployed AI Systems in Health Care
Timothy Keyes, Alison Callahan, Abby S. Pandya +18
Post-deployment monitoring of artificial intelligence (AI) systems in health care is essential to ensure their safety, quality, and sustained benefit-and to support governance deci…
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…
Standing on FURM ground -- A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems
Alison Callahan, Duncan McElfresh, Juan M. Banda +21
The impact of using artificial intelligence (AI) to guide patient care or operational processes is an interplay of the AI model's output, the decision-making protocol based on that…