Computational intelligence has demonstrated its capability and achieved great success in almost all fields, including medicine. For precision medicine, one of the central aspects is using computational intelligence, together with big data, to help guide clinical decisions. The widespread use of computational intelligence techniques like deep learning and neural networks in initial clinical applications can serve as a guide to a clearer understanding of what they are and are not, their promise, and their potential peril.

In recent years, more and more advanced computational intelligence algorithms are developed to assist and implemented in routine clinical practice. There are numerous examples of typical and advanced computational intelligence algorithms such as random forest, support vector machine, and deep learning in biomedical information processing. For instance, deep neural networks have been extensively applied to predict clinical risk via learning the characteristics and knowledge from large-scale and complex electronic health records datasets. In addition, other notable examples include prediction of cardiovascular disease risk using retinal fundus images, lung cancer prognosis using histopathology specimens, and classification of malignant or benign based on nevus pictures.

This Special Issue aims to collate original research and review articles with a focus on the latest developments of computational intelligence with applications in biomedical information processing, such as disease diagnostics, biomedical imaging analysis, biomedical research, and biological data processing. We hope to provide a comprehensive and up-to-date compilation of research and experimental works in the field. Moreover, we also welcome the contributions of clinical medical research related to disease diagnostics and treatment. The inclusion of experimental data is very much encouraged.

Potential topics include but are not limited to the following:

• Electronic health records data mining and processing
• CT image processing, segmentation, and classification
• MRI image processing, segmentation, and classification
• Medical data analysis
• Deep learning applications for medical data
• Advances in computational intelligent algorithms
• Computational intelligent algorithms for cardiovascular diseases
• Intelligent agents and systems for precision medicine
• Real-time healthcare monitoring and security alerting systems
• New models, protocols, and applications in healthcare analytics
• Artificial intelligence in multi-modal clinical data mining