Visualization, particularly scientific visualization, provides unbeatable mechanisms to communicate different aspects of data. It broadly includes several important computer science research fields, including computer graphics, computer vision, and visual computing. With the growing advances in artificial intelligence (AI), machine learning algorithms are increasingly being integrated with visualization and computer vision methods. Owing to the fact that new technologies enable many non-intrusive, wearable, multi-modal, and sensor-based devices to be used in healthcare-related data collection, the data obtained from these processes provides a promising opportunity for many visualization and computer vision methods to be put in place. Those data often have some unique characteristics, including sensitivity, high practical value, complexity, large size, and multi-dimensionality, which makes the research exploration even more intriguing. The combination of visualization, computer vision, and machine learning facilitates the creation of efficient approaches, applications, and even systems in healthcare.

Machine learning/AI-based computer vision methods have been developed for the diagnosis of tumors and nodules appearing in different human organs using image data obtained using different scan modalities, such as CT and MRI. Some of the results are promising, yet there is still room for improvement. Machine learning/AI techniques can be used for feature extraction and classification of non-image healthcare data, which is often neglected. The non-image data can include text-based patient records, doctors' prescriptions, medicine descriptions, and diagnosis results. This can be an important exploration of the capability of ML/AI, as visualization created for non-image healthcare data increases the practical value of the data.

This Special Issue aims to cover recent advancements in visualization and computer vision using machine learning and AI with a particular interest in healthcare data and its open problems. The objective is to provide a comprehensive and up-to-date collection of research and experimental works in the field. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Visualization-based predictive analytics and therapy
• Medical image processing and computer vision
• Novel visualization algorithms using healthcare data
• Machine learning-enriched visualization methods
• Single and multi-dimensional medical image analysis
• Visualization of e-health data
• Cloud and big data visualization for healthcare
• Clinical/patient record visualization
• Patient behavior data visualization and analysis
• Visualization and machine learning in assistive technology
• Visualization-aided diagnosis and prediction
• Symptom-related pattern detection and recognition
• Visualization-guided medical procedures
• Medical data (image and non-image) feature extraction