102 citations · 140 across the 5 of their papers we have counts for
6 papers
Large language models provide unsafe answers to patient-posed medical questions
Rachel L. Draelos, Samina Afreen, Barbara Blasko +14
Millions of patients are already using large language model (LLM) chatbots for medical advice on a regular basis, raising patient safety concerns. This physician-led red-teaming st…
The Cydoc smart patient intake form accelerates medical note writing
Angela Hemesath, Kenyon Wright, Matthew Michael Draelos +1
Purpose: This study evaluates the effect of Cydoc software tools on medical note time-to-completion and quality. Methods: Medical students were recruited by email to participate in…
Explainable multiple abnormality classification of chest CT volumes
Rachel Lea Draelos, Lawrence Carin
Understanding model predictions is critical in healthcare, to facilitate rapid verification of model correctness and to guard against use of models that exploit confounding variabl…
Playing Codenames with Language Graphs and Word Embeddings
Divya Koyyalagunta, Anna Sun, Rachel Lea Draelos +1
Although board games and video games have been studied for decades in artificial intelligence research, challenging word games remain relatively unexplored. Word games are not as c…
Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks
Rachel Lea Draelos, Lawrence Carin
Explanation methods facilitate the development of models that learn meaningful concepts and avoid exploiting spurious correlations. We illustrate a previously unrecognized limitati…
Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes
Rachel Lea Draelos, David Dov, Maciej A. Mazurowski +4
Machine learning models for radiology benefit from large-scale data sets with high quality labels for abnormalities. We curated and analyzed a chest computed tomography (CT) data s…