Zaid Nabulsi

TL;DR: An AI system to classify CXRs as normal or abnormal and the results suggest that the AI system trained using a large dataset containing a diverse array of CXR abnormalities generalizes to new patient populations and unseen diseases.

TL;DR: In this article, the authors developed and evaluated an AI system to classify chest radiography (CXR) as normal or abnormal, using a de-identified dataset of 248,445 patients from a multi-city hospital network in India.

TL;DR: Supervised contrastive learning enabled performance comparable to state-of-the-art deep learning models in multiple clinical tasks by using as few as 45 images and is a promising method for predictive modeling with use of small data sets and for predicting outcomes in shifting patient populations.

TL;DR: A deep learning method was found to be noninferior to radiologists for the determination of active tuberculosis on digital chest radiographs and its performance was compared with that of radiologists.

TL;DR: In this article, a deep learning system was trained to detect active pulmonary TB using CXRs from 9 countries across Africa, Asia, and Europe, and utilized large-scale CXR pretraining, attention pooling, and noisy student semi-supervised learning.