Machine Learning and Artificial Intelligence in Radiation On A Guide for Clinicians

Dr. Rosenstein is a Professor of Radiation Oncology and a Professor of Genetics & Genomic Sciences at the Icahn School of Medicine at Mount Sinai. He is a renowned educator who has received numerous teaching awards. More than 600 trainees from multiple radiation oncology and medical physics residency programs have taken Dr. Rosenstein's 60-hour course in radiation and cancer biology. He also provides lectures in radiation biology and radiation safety for radiology residents. Dr. Rosenstein has served as a member of the Radiation Oncology Radiation/Cancer Biology Committee and the Radiology Physics Committee of the American Board of Radiology. He created and was the editor-in-chief of the widely used American Society for Radiation Oncology (ASTRO) Radiation and Cancer Biology Practice Examination and Study Guide. The focus of Dr. Rosenstein's research program has been the identification of genetic/genomic markers associated with the development of adverse effects resulting from radiotherapy.

Dr. Rattay is Associate Professor in Breast Surgery at the University of Leicester and Consultant Breast Surgeon at University Hospitals of Leicester, UK. He has been working in the field of radiobiology and radiogenomics of the normal tissues for over ten years. His research is specifically focused on the effect of breast radiotherapy on surgical and patient-reported outcomes. This includes Big Data and machine learning (ML) approaches and he is also interested in applying qualitative research methodology to explore breast cancer survivors' views and experience of treatment and personalised medicine. Dr. Rattay works in a multi-disciplinary research team with nurses, clinical psychologists, geneticists, radiographers and medical physicists, and he has established collaborations with ML experts both nationally and internationally.

Dr. Kang has over 15 years of experience in developing and applying novel computational methods to complex, biomedical data. He is an assistant professor and biomedical informatics lead in the Dept. of Radiation Oncology at the University of Washington. His research focus is on machine learning in oncology with a specific focus on natural language processing and topic modeling and his operations focus is on using informatics to improve patient care and decrease physician burden. Dr. Kang has been invited to speak on AI in oncology at several national and international conferences and workshops.