Raymond Mak
FACULTY MEMBER

Raymond Mak MD is an Associate Professor of Radiation Oncology at Harvard Medical School and Director of Clinical Innovation, Thoracic Radiation Oncology Disease Site Leader, and Director of Patient Safety/Quality in the Department of Radiation Oncology at BWH/DFCI. Dr. Mak is a graduate of Cornell University and Harvard Medical School, and completed his residency training in the Harvard Radiation Oncology Residency Program.
His main research interests are to develop clinically-relevant biomarkers of both tumor response and radiation-induced toxicity after radiation therapy in lung cancer using AI and novel imaging technologies, and clinical application of AI solutions to improve the quality and effectiveness of cancer therapy through the design and implantation of clinical trials. His work has been recognized with funding from NCI (U01: Site PI), and the Radiological Society of North America.
email: rmak@partners.org
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
Bloomberg, WBUR, Yahoo and many other major news outlets feature AIM study on ChatGPT
AIM researchers investigate ChatGPT for its ability to provide cancer treatment recommendations
AIM investigators developed an oncology AI Fact Sheet to facilitate the safe translation of AI models into cancer clinics.
We developed an AI model that can accurately predict distant metastases after treatment for lung cancer patients.
In Lancet Digital Health, we published a clinical validation of deep learning algorithms to target lung cancer tumors.
In The Lancet Digital Health, AIM investigators have defined the levels of autonomy in medical AI.
In Nature Reviews Clinical Oncology, we highlight how AI is transforming the field of radiation oncology to treat patients more accurately and efficiently.
Our article "Deep learning for lung cancer prognostication" was selected by IMIA as one of the best articles of the year.
Dr. Raymond Mak is featured in an Harvard Business School news item about his AI work.
Our study on applying crowd innovation to develop deep learning models was featured by Reuters.
JAMA Oncology published our investigation of deep learning methods for lung tumor segmentation.
Clinical Cancer Research published about deep learning applied to serial imaging to improve outcome predictions.
Our publication about artificial intelligence in cancer imaging was highlighted on the cover of CA: A Cancer Journal for clinicians.
JNCI published our study about trial design of novel technologies for cancer treatment, including artificial intelligence algorithms.
A review of AI applications in the imaging of several tumor types has been published in CA: A Cancer Journal for clinicians.
PLOS Medicine published our study exploring deep learning for predicting overall survival in lung cancer patients.