Evan J. Zucker

TL;DR: CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs, achieved radiologist-level performance on 11 pathologies and did not achieve radiologists' level performance on 3 pathologies.

TL;DR: A deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams is developed and the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation is supported.

TL;DR: PDUFA deadlines have appreciably changed the approval decisions of the FDA, and once medications are in clinical use, the discovery of safety problems is more likely for drugs approved immediately before a deadline than for those approved at other times.

TL;DR: The superior diagnostic accuracy of the combined HITL AI solution compared to radiologists and AI alone has broad implications for the surging clinical AI deployment and implementation strategies in future practice.

TL;DR: Key clinical features, multimodality imaging findings, and treatment options of venous compression syndromes are reviewed, with emphasis on the growing role of noninvasive imaging options such as magnetic resonance venography (MRV).