Visualization, particularly scientific visualization, provides unbeatable mechanisms to communicate different aspects of data. It broadly includes several important computer science research fields, e.g. computer graphics, computer vision and visual computing. With the growing advancement of artificial intelligence, machine learning algorithms are more and more being integrated with visualization and computer vision methods. Owing to the fact that technologies enable many non-intrusive, wearable, multi-modal and sensor-based devices used in data collection of healthcare-related activities and solutions, data obtained in these processes provide decent test fields and playgrounds for many visualisations and computer vision methods put in place. Those data usually bear with some unique characteristics, e.g. sensitive, with high practical value, complex, huge in size and multi-dimensional, which make 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 diagnoses of tumours and nodules appearing in different human organs using image data obtained using different scan modalities, e.g., CT and MRI. Some of the results are promising, yet there are still spaces for improvement. Machine learning/AI techniques can be used for feature extraction and classification for non-image healthcare data which is often neglected. Those non-image data includes text-based patient records, doctors' prescriptions, medicine descriptions, diagnosis results and so on. This can be an important exploration of the capability of ML/AI. Visualisation created for non-image healthcare data increases the practical value of those data.

With a special interest in healthcare data and its open problems, the special issue aims to cover the recent advancement in visualization and computer vision using machine learning and AI in the application area of healthcare. The objective is to provide a comprehensive and latest collection of research and experiment works in the field.

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 behaviour data visualization and analysis
• Visualization and machine learning in Assistive Technology
• Visualization-aided diagnosis and prediction
• Symptoms-related patterns detection and recognition
• visualization-guided medical procedures
• Medical data(image and non-image) feature extraction