Policy Points: r With increasing integration of artificial intelligence and machine learning in
medicine, there are concerns that algorithm inaccuracy could lead to patient injury and …

In this article, the authors propose an ethical framework for using and sharing clinical data
for the development of artificial intelligence (AI) applications. The philosophical premise is …

The use of Artificial Intelligence and machine learning in basic research and clinical
neuroscience is increasing. AI methods enable the interpretation of large multimodal …

Deep learning of artificial neural networks has become the de facto standard approach to
solving data analysis problems in virtually all fields of science and engineering. Also in …

Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the
most common radiographic findings in dentistry and have a differential diagnosis including …

Deep learning has achieved remarkably positive results and impacts on medical diagnostics
in recent years. Due to its use in several proposals, deep learning has reached sufficient …

Simple Summary The two most common cross-sectional imaging modalities, computed
tomography (CT) and magnetic resonance imaging (MRI), have shown enormous utility in …

To enhance the quality and efficiency of information processing and decision-making,
automation based on artificial intelligence and machine learning has increasingly been …

The ability to dynamically extend a model to new data and classes is critical for multiple
organ and tumor segmentation. However, due to privacy regulations, accessing previous …

Please see the Editorial Comment by Sadia Khanani discussing this article. Artificial
intelligence (AI) applications for screening mammography are being marketed for clinical …