  Purpose of review: Artificial intelligence (AI) has become popular in medical
applications, specifically as a clinical support tool for computer-aided
diagnosis. These tools are typically employed on medical data (i.e., image,
molecular data, clinical variables, etc.) and used the statistical and machine
learning methods to measure the model performance. In this review, we
summarized and discussed the most recent radiomic pipeline used for clinical
analysis. Recent findings:Currently, limited management of cancers benefits
from artificial intelligence, mostly related to a computer-aided diagnosis that
avoids a biopsy analysis that presents additional risks and costs. Most AI
tools are based on imaging features, known as radiomic analysis that can be
refined into predictive models in non-invasively acquired imaging data. This
review explores the progress of AI-based radiomic tools for clinical
applications with a brief description of necessary technical steps. Explaining
new radiomic approaches based on deep learning techniques will explain how the
new radiomic models (deep radiomic analysis) can benefit from deep
convolutional neural networks and be applied on limited data sets. Summary: To
consider the radiomic algorithms, further investigations are recommended to
involve deep learning in radiomic models with additional validation steps on
various cancer types.
