Artificial intelligence (AI) and its applications are among the most investigated research areas. Over recent years, we have witnessed AI revolutionising all kinds of medical imaging, including X-ray, ultrasound, computerised tomography (CT), MRI, fMRI, positron emission tomography (PET), and single photon emission computed tomography (SPECT). Numerous AI-based tools have been developed to automate medical image analysis and improve automated image interpretation.

Modern medical imaging provides an increasing number of features derived from different types of analysis, including artificial intelligence. These features are most often used for a variety of analyses, including deep learning, fuzzy sets, rough sets, uncertain analysis, multi-objective optimisation, swarm intelligence optimisation, and machine learning. The results of these analyses can be used as a reference for the evaluation of patients by medical teams. A further challenge of AI-driven solutions is the development of tools for personalised disease assessment through AI models by taking advantage of their ability to learn patterns and relationships in medical images, utilising massive volumes of medical images.

This Special Issue aims to promote the latest cutting-edge AI-driven research in medical image processing and analysis, with the aforementioned approaches in mind. Of particular interest are submissions regarding novel algorithms, architectures, techniques, and applications of AI for medical image analysis. However, contributions concerning other aspects of medical image processing (including, but not limited to, image quality improvement, image reconstruction, image restoration, image registration, image segmentation, and image feature extraction, to tackle the variations in image spatial-temporal resolution, as well as the diversity of biophysical-biochemical mechanisms) are also welcomed. We invite investigators to contribute original research articles as well as review articles that will address the challenges facing artificial intelligence approaches in medical image analysis.

Potential topics include but are not limited to the following:

• Deep learning models for medical image reconstruction, restoration, registration, segmentation, classification, visualisation, and prediction
• Fuzzy sets (fuzzy relations) in medical image analysis
• Rough sets in medical image analysis
• Uncertain analysis in medical image analysis
• Multi-objective optimisation in medical image analysis
• Swarm intelligence optimisation in medical image analysis
• Machine learning-based integrated concepts and solutions in medical image analysis
• Data security and user privacy solutions for medical image analysis