The digital transformation in healthcare, propelled by the integration of deep learning
models and the Internet of Things (IoT), is creating unprecedented opportunities for …

There are increasing concerns about the bias and fairness of artificial intelligence (AI)
models as they are put into clinical practice. Among the steps for implementing machine …

The increasing use of machine learning (ML) algorithms in clinical settings raises concerns
about bias in ML models. Bias can arise at any step of ML creation, including data handling …

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical
diagnoses and research which underpin many recent breakthroughs in medicine and …

Single image super-resolution (SISR) has played an important role in the field of image
processing. Recent generative adversarial networks (GANs) can achieve excellent results …

In recent years, deep learning (DL) has shown impressive performance in radiologic image
analysis. However, for a DL model to be useful in a real-world setting, its confidence in a …

Super-resolution of brain magnetic resonance imaging (MRI) generates high resolution
brain images as opposed to low-resolution ones, thus providing more detailed anatomical …

The incorporation of physical information in machine learning frameworks is opening and
transforming many application domains. Here the learning process is augmented through …

Robust machine learning models based on radiomic features might allow for accurate
diagnosis, prognosis, and medical decision-making. Unfortunately, the lack of standardized …

The integration of artificial intelligence (AI) into medical imaging has guided in an era of
transformation in healthcare. This literature review explores the latest innovations and …