Deep learning, a sophisticated type of AI in which the computer can be trained to recognize subtle patterns, has the potential to improve chest X-ray interpretation, said scientists.

Researchers at Google Health developed deep learning models for chest X-ray interpretation that overcome some of these limitations. They used two large datasets to develop, train and test the models.

Assistance from artificial intelligence enhanced nonradiologists’ ability to detect lung lesions on chest radiographs, according to study results published in Annals of the American Thoracic ...

Use of the 2015 Pediatric Acute Lung Injury Consensus Conference, or PALICC, criteria for diagnosing pediatric acute respiratory distress syndrome mostly resulted in agreement between two ...

In the present study, the team developed an AI tool for the interpretation of chest radiographs and conducted a retrospective evaluation of its performance in the emergency department setting.

Researchers at Google Health developed deep learning models for chest X-ray interpretation that overcome some of these limitations. They used two large datasets to develop, train and test the models.

Chest radiographs of children in which 3 or more radiologists changed their interpretation in regard to the presence or absence of an alveolar infiltrate with the addition of clinical information.

An AI chest x-ray reader could provide near instantaneous interpretations. And AIs will never need to take lunch breaks or get distracted by noisy working conditions or complain about working 24 ...

Objective The objective of the study was to determine the impact of clinical history on chest radiograph interpretation in the diagnosis of pneumonia. Design Prospective case-based study.