The journal of Artificial Intelligence In Oncology (AIO, Artif. Intell. Oncol., ISSN: 2767-2883; DOI prefix: 10.52454), established in 2017, serves as a platform for the publication of academic and industry-related papers in  AI and its application in medical oncology, radiation oncology, surgical oncology, and basic cancer research. The journal is particularly interested in publishing conceptualization, algorithms, and reasoning that will promote the application of AI in early cancer diagnosis, automated image segmentation, personalized radiotherapy and drug screening. 

The inherent complexity of human malignancies calls for the development of cutting-edge technologies, concepts and methods that will eventually be applied in the diagnosis and treatment of cancer patients. The journal of AIO is established to provide the unmet academic requirements in publication, evaluation, and quality control of AI's applications in oncology. To ensure high-quality research paper publications from global talents with or without fundings, the journal is FREE of charge for submission, peer-review and publication online. 

Aims and Scope 

Artificial Intelligence in Oncology covers a broad area related with the application of artificial intelligence in cancer management. Particular attention is given to manuscripts pertaining to:

  • AI-based cancer diagnosis;
  • AI-based design of treatment plans;
  • Data-mining and algorithms of cancer epidemiology;
  • AI-based analysis of genomics;
  • Data mining of genomics;
  • Agent-based systems and application in medical oncology;
  • AI-based evaluation of bio-markers;
  • Conceptualization of AI methodologies in oncology;
  • AI-based diagnosis and case studies;
  • Machine learning and cancer patient care;
  • Automated patient-machine interface;
  • Automated positron emission tomography/ computed tomography (PET/CT) contouring;
  • Automated evidence-based chemo-/radio-dosage suggestions;
  • Automated radiation prescription;
  • Automated PET/CT/MRI image analysis



An Overview of Mathematical Models for RNA Sequence-based Glioblastoma Subclassification

Yilin Wu, Eric Zander, Andrew Ardeleanu, Ryan Singleton, Barnabas Bede


AI in oncology: when science fiction meets reality

Bin Li, Ph.D.

Mining Cancer-related Information in Electronic Healthcare Records with Natural Language Processing

Man Liu, M.S.

Towards the personalized radiotherapy for elderly and/or frail glioblastoma multiforme patients – radiosensitivity profiling using biomarkers and image analyses

Shuhua Zheng, Yilin Wu

A Cascaded U-net for Kidney and Tumor Segmentation from CT volumes

Asha K Kumaraswamy, Chandrashekar Patil

Application of artificial intelligence in cancer patient care during COVID-19 pandemic

Shuhua Zheng, Yue MENG