Abstract The use of Artificial Intelligence (AI) in Digital technologies (DT) is proliferating a
profound socio-technical transformation. Governments and AI scholarship have endorsed …

The scale of genetic, epigenomic, transcriptomic, cheminformatic and proteomic data
available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the …

Purpose Artificial intelligence (AI) models are playing an increasing role in biomedical
research and healthcare services. This review focuses on challenges points to be clarified …

Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …

The automatic generation of radiology reports has the potential to assist radiologists in the
time-consuming task of report writing. Existing methods generate the full report from image …

Study Design Item 5. Indicate if the study is retrospective or prospective. Evaluate predictive
models in a prospective setting, if possible. Item 6. Define the study's goal, such as model …

As artificial intelligence (AI) systems begin to make their way into clinical radiology practice,
it is crucial to assure that they function correctly and that they gain the trust of experts …

The coronavirus disease 2019 (COVID-19) pandemic has imposed dramatic challenges to
health-care organizations worldwide. To combat the global crisis, the use of thoracic …

As the number of Internet-connected systems rises, cyber analysts find it increasingly difficult
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …

Deep learning as performed by artificial deep neural networks (DNNs) has achieved great
successes recently in many important areas that deal with text, images, videos, graphs, and …