Artificial intelligence (AI) has contributed substantially to the resolution of a variety of
biomedical problems, including cancer, over the past decade. Deep learning, a subfield of AI …

This article is a comprehensive review of the basic background, technique, and clinical
applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A …

We developed and validated a deep learning (DL)-based model using the segmentation
method and assessed its ability to detect lung cancer on chest radiographs. Chest …

Automatic liver and tumor segmentation play a significant role in clinical interpretation and
treatment planning of hepatic diseases. To segment liver and tumor manually from the …

In this review, we address the issue of fairness in the clinical integration of artificial
intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a …

The application of machine learning and deep learning in the field of imaging is rapidly
growing. Although the principles of machine and deep learning are unfamiliar to the majority …

The clinical team attending to a patient upon a diagnosis is faced with two main questions:
what treatment, and at what dose? Clinical trials' results provide the basis for guidance and …

Background Deep learning (DL), a subset of artificial intelligence (AI), has been applied to
pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been …

Automatic liver and tumor segmentation are essential steps to take decisive action in hepatic
disease detection, deciding therapeutic planning, and post-treatment assessment. The …

The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit
detail from large datasets have attracted substantial research attention in the field of medical …