In the modern development of cancer management, radiation oncology is uniquely positioned as the “High Tech Medicine”. Artificial Intelligence (AI) related developments such as smarter machines and integrated patient outcome analysis have made superior progress in the last few years. The automation and sophisticated big data applications in the radiation world have drawn a new era of treating cancer patients with precision and outcome prediction.

There are many other new developments such as machine learning, outcome analysis, radiomics of early cancer detection, and other deep learning related successes in big data medicine in the recent radiation oncology world. With increasing computing power and treatment accuracy in fighting cancers, applying this in radiation oncology treatment could lead to huge progress in increasing patient survival rate. The application of AI not only provides substantial cancer survival logistics but may reduce the patient complication treated by conventional methodologies. Integration of AI radiation therapy technologies to manage tumor control certainly creates important scientific interests with current scientists and clinicians.

The aim of this Special Issue is to collate original research and review articles focusing on new developments in cancer treatment, big data analysis, summaries of machine learning, deep learning processes, patient outcome prediction, and treatment technique improvements with automation, as well as radiomics and cancer pattern recognition with related topics. We particularly welcome submissions focused on clinical progress with new technologies in the AI world.

Potential topics include but are not limited to the following:

• Clinical trials and outcome research using radiomics
• Automation and implementation of smart treatment machines
• QA automation using big data of treatment facility
• Machine learning in radiation therapy
• Deep learning in radiation therapy
• Analysis of patient treatment information and outcome prediction with big data structures
• Prediction of treatment uncertainty and accuracy from machine automation
• Artificial intelligence in improving treatment process with smart machinery
• Artificial intelligence in radiation oncology