OBJECTIVE: To compare the performance of three artificial intelligence (AI) classification strategies against manually classified National Institutes of Health (NIH) cardiac arrest (CA) grants, with the goal of developing a publicly available tool to...
BACKGROUND: In recent years, deep learning methods have been successfully used for chest x-ray diagnosis. However, such deep learning models often contain millions of trainable parameters and have high computation demands. As a result, providing the ...
PURPOSE OF REVIEW: This review highlights the artificial intelligence, machine learning, and deep learning initiatives supported by the National Institutes of Health (NIH) and the National Eye Institute (NEI) and calls attention to activities and goa...
OBJECTIVE: To study the longitudinal performance of fully automated cartilage segmentation in knees with radiographic osteoarthritis (OA), we evaluated the sensitivity to change in progressor knees from the Foundation for the National Institutes of H...
IMPORTANCE: Despite the rapid growth of interest and diversity in applications of artificial intelligence (AI) to biomedical research, there are limited objective ways to characterize the potential for use of AI in clinical practice.
Aging is universal, yet characterizing the molecular changes that occur in aging which lead to an increased risk for neurological disease remains a challenging problem. Aging affects the prefrontal cortex (PFC), which governs executive function, lear...
Journal of the American Medical Informatics Association : JAMIA
Aug 13, 2021
OBJECTIVE: To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling.