In the field of artificial intelligence (AI)-based techniques, large data sets are used to make predictions of future events. A potential application of AI-based techniques in the medical field is the development of assistive clinical tools for detecting and predicting perilous diseases. Similarly, machine learning (ML) and deep learning (DL) technology that requires less user input but has the potential to process a large amount of data and can be more powerful, has increased physicians’ ability to diagnose patients accurately. ML/DL has found applications in a number of fields of research including genomes, medical image processing, disease prediction, and health informatics.

Currently, researchers are focusing on the prediction of genomics and proteomics using ML/DL algorithms. The size of genome databases is increasing day by day due to the new discovery of DNA, RNA, and protein sequences. Although the biochemical experimental methods have achieved relatively good results, they are time-consuming and costly, especially when it comes to time and money. Therefore, it is important to introduce an AI-based computational model for health informatics.

In order to achieve its objectives, this Special Issue aims to collect original research and review articles that describe advances in this field.

Potential topics include but are not limited to the following:

• AI-based clinical decision making
• AI in healthcare and medical education
• Mathematical, computational, and statistical modeling
• Benchmark and data analysis
• Data mining and health informatics
• Computational biology
• Medical knowledge engineering
• Knowledge-based systems
• Biomedical informatics
• Computational intelligence in biology
• Natural language processing (NLP) in healthcare
• AI and data science in medicine, medically-oriented human biology, and healthcare