Imaging and Radiation Research

Artificial Intelligence in Radiation Oncology and Biomedical Physics

Submission deadline: 2024-04-30
Section Editors

Section Collection Information

Dear Colleagues,

Artificial Intelligence (AI) emulates or augments human intelligence and in this parlance it has already gained much recent attention and established its role in almost all walks of our digital life. Availability of high speed microprocessors have provided us enough computing power to process high dimensional large data sets in real time making smooth development, testing and deploying of reliable and robust machine learning models in resource hungry applications possible and cost effective. A huge potential of application of AI tools in medicine in general and oncology in particular is on the horizon. From research on historical data, epidemiological predictions based on time series or real time application in patient care, the role of AI in diagnosis and treatment can bring unprecedented changes in healthcare industry. As in every field in medicine, the use of AI is in its early phase since the demand from various AI tools in terms of its implementation is not only to provide assistance to the therapists, dosimetrists, physicists, nurses, technologists, and managers but making the radiation oncologists more precise in achieving personalized and improved outcomes in the backdrop of risk stratification.


Radiation oncology is one of the most complex branch of medicine which involves practicing extreme precision in imaging, treatment, planning, simulation, targeting, and quality assurance at the point of care. However, implementation of AI tools and techniques in radiation oncology and biomedical physics is fraught with challenges. There is an urgent need to conduct high quality extra-mural collaborative research and share the results with the scientific and medical community in order to ensure the smooth implementation of that this new technology in a field where it is needed the most.         


Artificial Intelligence; Machine Learning; Deep Learning; Radiation Oncology; Biomedical Physics; Radiation Therapy