60 citations
- United States Food and Drug AdministrationUS9 papers
- Office of ScienceUS5 papers
- University of ChicagoUS3 papers
- University of North Carolina at Chapel HillUS3 papers
- Argonne National LaboratoryUS2 papers
- Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentUS2 papers
- George Mason UniversityUS2 papers
- Goddard Space Flight CenterUS2 papers
- Los Alamos National LaboratoryUS2 papers
- Michigan State UniversityUS2 papers
- New York UniversityUS2 papers
- San Diego State UniversityUS2 papers
17 papers
sFRC for assessing hallucinations in medical image restoration
Prabhat Kc, Rongping Zeng, Nirmal Soni +1
Deep learning (DL) methods are currently being explored to restore images from sparse-view-, limited-data-, and undersampled-based acquisitions in medical applications. Although ou…
Impact of AI-Triage on Radiologist Report Turnaround Time: Real-World Time-Savings and Insights from Model Predictions
Yee Lam Elim Thompson, Jonathan Fergus, Jonathan Chung +4
Objective: To quantify the impact of workflow parameters on time-savings in report turnaround time (TAT) due to an AI-triage device that prioritized pulmonary embolism (PE) in ches…
Performance Assessment Strategies for Language Model Applications in Healthcare
Victor Garcia, Mariia Sidulova, Aldo Badano
Language models (LMs) represent an emerging paradigm within artificial intelligence, with applications throughout the medical enterprise. A comprehensive understanding of the clini…
A sharp interface Lagrangian-Eulerian method for flexible-body fluid-structure interaction
Ebrahim M. Kolahdouz, David R. Wells, Simone Rossi +3
This paper introduces a sharp-interface approach to simulating fluid-structure interaction involving flexible bodies described by general nonlinear material models and across a bro…
Objective task-based evaluation of artificial intelligence-based medical imaging methods: Framework, strategies and role of the physician
Abhinav K. Jha, Kyle J. Myers, Nancy A. Obuchowski +5
Artificial intelligence (AI)-based methods are showing promise in multiple medical-imaging applications. Thus, there is substantial interest in clinical translation of these method…
A practical model-based segmentation approach for improved activation detection in single-subject functional Magnetic Resonance Imaging studies
Wei-Chen Chen, Ranjan Maitra
Functional Magnetic Resonance Imaging (fMRI) maps cerebral activation in response to stimuli but this activation is often difficult to detect, especially in low-signal contexts and…