The hype over artificial intelligence (AI) has spawned claims that clinicians (particularly radiologists) will become redundant. It is still moot as to whether AI will replace radiologists in day-to-day clinical practice, but more AI applications are...
The advent of Deep Learning (DL) is poised to dramatically change the delivery of healthcare in the near future. Not only has DL profoundly affected the healthcare industry it has also influenced global businesses. Within a span of very few years, ad...
Artificial intelligence (AI) involves computational networks (neural networks) that simulate human intelligence. The incorporation of AI in radiology will help in dealing with the tedious, repetitive, time-consuming job of detecting relevant findings...
AJR. American journal of roentgenology
Jan 30, 2019
OBJECTIVE: Radiology reports are rich resources for biomedical researchers. Before utilization of radiology reports, experts must manually review these reports to identify the categories. In fact, automatically categorizing electronic medical record ...
Deep learning has rapidly advanced in various fields within the past few years and has recently gained particular attention in the radiology community. This article provides an introduction to deep learning technology and presents the stages that are...
BACKGROUND: Quantizing the Breast Imaging Reporting and Data System (BI-RADS) criteria into different categories with the single ultrasound modality has always been a challenge. To achieve this, we proposed a two-stage grading system to automatically...
Due to the exponential growth of computational algorithms, artificial intelligence (AI) methods are poised to improve the precision of diagnostic and therapeutic methods in medicine. The field of radiomics in neuro-oncology has been and will likely c...