Samuel Luk, Ph.D. is an Assistant Professor of Radiation Oncology at Chobanian and Avedisian School of Medicine, Boston, MA, USA and a Medical Physicist at the Department of Radiation Oncology at Boston Medical Center.

Sam received his Ph.D. from the University of Arizona under the supervision of Professor Rolf Binder and was a postdoctoral fellow at the University of Washington with Professor Alan Kalet. He completed his medical physics residency training at the University of Washington and is board certified at American Board of Radiology – Therapeutic Medical Physics.

Sam’s research focus is on using artificial intelligence (AI) and Bayesian approach to detect potential errors in radiation oncology to ensure a safe and efficient delivery of radiotherapy treatments to cancer patients. One of the projects he has worked on is a Bayesian network-based error detection (EDBN) model to detect potential erroneous treatment plan parameters in radiotherapy during physics plan review. The core concept of the EDBN project is to build an AI model to detect potential errors in radiotherapy treatment plans, which is rare but could lead to reduction of therapeutic effects and increase normal tissue toxicity. He is also interested in data science in health care and currently working on a collaborative project to compare radiation oncology clinical practices among clinics from different countries.

Sam has published 17 peer-reviewed original research and review articles in condensed matter and medical physics and has been invited to multiple conferences to discuss AI and quality assurance. He is a member of American Association of Physicists in Medicine (AAPM) and volunteering in the work group of prevention of error in radiation oncology and machine intelligence subcommittee. He is also a member of the American Society of Radiation Oncology.