Recent studies have used speech signals to assess depression. However, speech features
can lead to serious privacy concerns. To address these concerns, prior work has used …

Background Data used for training of deep learning networks usually needs large amounts
of accurate labels. These labels are usually extracted from reports using natural language …

Artificial intelligence (AI) in radiology has gained wide interest due to the development of
neural network architectures with high performance in computer vision related tasks. As AI …

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The
relationship between external risk factors and our genetics have not been well established. It …

Background There is growing concern about the impact of artificial intelligence (AI) on
radiology and the future of the profession. The aim of this study is to evaluate general …

Artificial intelligence (AI) tools are rapidly being developed for radiology and other clinical
areas. These tools have the potential to dramatically change clinical practice; however, for …

This statement from the European Society of Thoracic imaging (ESTI) explains and
summarises the essentials for understanding and implementing Artificial intelligence (AI) in …

Background Abstraction of critical data from unstructured radiologic reports using natural
language processing (NLP) is a powerful tool to automate the detection of important clinical …

Evidence‐based medicine, outcomes management, and multidisciplinary systems are laying
the foundation for radiology on the cusp of a new day. Environmental and operational forces …

Background: There is intense interest and speculation in the application of artificial
intelligence (AI) to radiology. The goals of this investigation were (1) to assess thoracic …