Recent advances in engineering and computational intelligence domains have helped to provide possible solutions for a number of real world problems. For example, the implementation of automated and semi-automated computational intelligence techniques for the assessment of medical data has helped to reduce disease burden compared to existing traditional techniques.

Disease in humans is growing rapidly, due to various unavoidable and uncontrollable reasons, and these unidentified and untreated disorders can lead to serious problems, including death. It is therefore of utmost importance to investigate how recent progress in clinical, scientific, and engineering domains can be applied to improve the identification and treatment of these disorders with the help of appropriate medical data. Common medical data, such as patient information and disease history, medical images recorded using a chosen modality, and bio-signals collected using a chosen electrode can be evaluated using semi-automated and automated techniques in order to diagnose the disorder with better accuracy. Bio-signal and bio-image assisted detection techniques are commonly followed in hospitals to detect disorders and the outcome attained from these diagnoses will help the doctors to plan for an appropriate treatment procedure to control and cure the disorder.

This Special Issue aims to collect innovative research and review articles on recent developments in the use of automated and semi-automated computational intelligence techniques in the assessment of medical data, including pre-screening/post-screening of patients with traditional evaluation methods, bio-signal based examination, and medical image-assisted evaluation, among others. The focus should be on clinical level assessment, machine-learning assisted examination, and deep-learning systems.

Potential topics include but are not limited to the following:

• Brain condition monitoring using EEG
• Brain condition monitoring using imaging techniques (fMRI, MRI, CT, and PET)
• Stroke detection
• Tumor detection
• Application of deep-learning and machine-learning systems in examining brain MRI