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 …
T Liu, E Siegel, D Shen - Annual review of biomedical …, 2022 - annualreviews.org
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 …