Benjamin Kann
Faculty Member
Benjamin H. Kann MD is Assistant Professor of Radiation Oncology at Harvard Medical School and clinical faculty at Dana-Farber Cancer Institute/Brigham and Women’s Hospital. He is a graduate of Tufts University School of Engineering with degrees in electrical and biomedical engineering. He received his M.D. from the Icahn School of Medicine at Mount Sinai and completed residency in Radiation Oncology at Yale School of Medicine.
His research is focused on the development and application of machine learning and deep neural networks for cancer imaging analysis and the development of digital biomarkers to predict clinical outcomes. He is interested in the use of artificial intelligence and cancer imaging to develop clinical decision-making tools that advance personalized cancer care and can be effectively translated into the clinic. His work has been funded by the NIH/NIDCR, Eastern Cooperative Oncology Group-American College of Radiology Imaging Network, Radiological Society of North America, and the Joint Center for Radiation Therapy foundation
email: benjamin_kann@dfci.harvard.edu
Research Highlights
Researchers at AIM investigated the use of LLMs for patient portal messaging.
AIM study investigates if AI can highlight social determinants of health from clinical notes
AIM investigators developed AI to track muscle mass for children through young adulthood
AIM researchers investigate ChatGPT for its ability to provide cancer treatment recommendations
AIM researchers developed AI that can diagnose sarcopenia in head and neck cancer.
AIM investigators published a clinical evaluation of AI algorithms to screen for extranodal-extension on CT.
We developed an AI model that can accurately predict distant metastases after treatment for lung cancer patients.
AIM investigators found that clinical trials with AI algorithms showed high variability in quality.
In Lancet Digital Health, we published a clinical validation of deep learning algorithms to target lung cancer tumors.
In Cancer Cell, we published our perspective on the impact of AI in Clinical Oncology.
In Nature Reviews Clinical Oncology, we highlight how AI is transforming the field of radiation oncology to treat patients more accurately and efficiently.
Deep learning identifies extranodal extension in head and neck cancer better than radiologists; potential to be used for treatment decisions.