Course presenters

Learn from world-renowned artificial intelligence experts when you attend Radiology in the Age of AI, our first-ever U.S.-based Spotlight Course. Engage with a panel of industry thought leaders on the forefront of AI research in medical imaging and ask them your questions.

Keynote Speakers


Andrew Y. Ng, PhD

Dr. Andrew Ng, PhD, a globally recognized leader in AI, is CEO of Landing AI and a General Partner at AI Fund. As the former Chief Scientist at Baidu and the founding lead of Google Brain, he led the AI Transformation of two of the world’s leading technology companies. A longtime advocate of accessible education, Dr. Ng is the Co-founder of Coursera, an online learning platform, and founder of, an AI education platform. Dr. Ng is also an Adjunct Professor at Stanford University’s Computer Science Department.


Lloyd B. Minor, MD

Lloyd B. Minor, MD, is the Carl and Elizabeth Naumann Dean of the Stanford University School of Medicine. With his leadership, Stanford Medicine has established a strategic vision to lead the biomedical revolution in Precision Health, a fundamental shift to more proactive and personalized health care. Dr. Minor is also a professor of Otolaryngology–Head and Neck Surgery and a professor of Bioengineering and of Neurobiology, by courtesy, at Stanford University. With more than 140 published articles and chapters, he is an expert in balance and inner ear disorders and was elected to the National Academy of Medicine in 2012.

RSNA Board Advisor


Curtis P. Langlotz, MD, PhD

Curtis P. Langlotz, MD, PhD, is Professor of Radiology and Biomedical Informatics and Director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center) at Stanford University. As a Medical Informatics Director for Stanford Health Care, he is also responsible for the computer technology that supports the Stanford Radiology practice. The AIMI Center develops artificial intelligence methods that enable computer systems to draw complex inferences from image information and associated clinical data to augment the skills of human imaging professionals. He currently serves on the Board of Directors of the RSNA as Liaison for Informatics.

Course Directors


Udo Hoffmann, MD, MPH

Udo Hoffmann, MD, MPH, is a Professor of Radiology at Harvard Medical School, Chief of the Division of Cardiovascular Imaging at Massachusetts General Hospital in Boston, MA and the Director of the MGH Cardiac MR PET CT Program. Dr. Hoffmann leads a vibrant multidisciplinary clinical and research group focused on utilizing advanced cardiovascular imaging to improve prevention, diagnosis and treatment of CV disease. Dr. Hoffmann has more than 450 original publications, many of them in top journals such as NEJM, JAMA, JACC, and Circulation. Key clinical trials and cohorts include ROMICAT, PROMISE, REPRIEVE and CT imaging in the FHS. Dr. Hoffmann is the PI of a successful NHLBI T32 training program and has trained many current leaders in CV imaging.


Matthew P. Lungren, MD, MPH

Dr. Lungren is the Associate Director of the Stanford Center for Artificial Intelligence in Medicine and Imaging and an Assistant Professor Clinician Scientist at Stanford University Medical Center. His leading research interest is in the field of machine learning and deep learning in medical imaging and clinical informatics. He holds an MPH, which focuses on his primary interest in biostatistical modeling in epidemiology and health policy in the national health care landscape and has extensive applied experience in both statistical and machine learning applications for solving important medical imaging challenges.

Expert Presenters


Hugo Aerts, PhD

Dr. Aerts is Associate Professor at Harvard Medical School and Director of the Computational Imaging and Bioinformatics Laboratory (CIBL) at the Dana-Farber Cancer Hospital. Dr. Aerts’ group focuses on the development and application of advanced computational approaches applied to medical imaging data, pathology and genomic data. Furthermore, he is a PI-member of the Quantitative Imaging Network (QIN) and Informatics Technology for Cancer Research (ITCR) initiatives of the NIH.


Safwan Halabi, MD

Safwan Halabi, MD is a Clinical Associate Professor of Radiology at the Stanford University School of Medicine and serves as the Medical Director for Radiology Informatics at Stanford Children's Health. He is board-certified in Radiology with a Certificate of Added Qualification in Pediatric Radiology. He is also board-certified in Clinical Informatics. He clinically practices obstetric and pediatric imaging at Lucile Packard Children's Hospital. Dr. Halabi’s clinical and administrative leadership roles are directed at improving quality of care, efficiency, and patient safety. He has also led strategic efforts to improve the enterprise imaging platforms at Stanford Children’s Health. He is a strong advocate of patient-centric care and has helped guide policies for radiology report and image release to patients. He has published in peer-reviewed journals on various clinical and informatics topics. His current academic and research interests include imaging informatics, deep/machine learning in imaging, artificial intelligence in medicine, clinical decision support and patient-centric health care delivery. He is currently leading the RSNA Informatics Data Science Committee and serves as a Board Member for the Society for Imaging Informatics in Medicine.


Hugh Harvey MBBS BSc

Dr. Harvey is a board certified radiologist and clinical academic, trained in the NHS and Europe’s leading cancer research institute, the ICR, where he was twice awarded Science Writer of the Year. Previously a consultant radiologist at Guy's and St. Thomas’ in London, he is now the clinical director at Kheiron Medical, an AI company focused on deep learning in breast cancer. He advises the Royal College of Radiologists informatics committee and AI advisory boards, and is also co-chair of the UK government Topol Review into AI in health care.


Charles E. Kahn Jr., MD

Dr. Kahn is Professor and Vice Chairman of Radiology, Perelman School of Medicine, and Senior Fellow of the Institute for Biomedical Informatics and the Leonard Davis Institute of Health Economics, University of Pennsylvania. He previously served as associate editor for Radiology and the American Journal of Roentgenology (AJR), section editor for the Yearbook of Medical Informatics, guest editor for the Journal of the American College of Radiology (JACR), and chair of the Publications Committee of the American Roentgen Ray Society (ARRS). Dr. Kahn has authored or co-authored more than 110 peer-reviewed articles and given nearly 100 invited lectures.


Jayashree Kalpathy-Cramer, MS, PhD

Jayashree Kalpathy-Cramer is the Director of the QTIM lab and the Center for Machine Learning at the Athinoula A. Martinos Center for Biomedical Imaging and an Associate Professor of Radiology at MGH/Harvard Medical School. An electrical engineer by training, she worked in the semiconductor industry. After returning to academia, she is now focused on the applications of machine learning and modeling in health care. Her research interests include medical image analysis, machine learning and artificial intelligence for applications in radiology, oncology and ophthalmology. The work in her lab spans the spectrum from novel algorithm development to clinical deployment. She is passionate about the potential these techniques have to improve access to health care in the U.S. and worldwide.


David B. Larson, MD, MBA

David B. Larson, MD, MBA, is the Vice Chair for Education and Clinical Operations in the Department of Radiology at Stanford University. He is a national thought leader in radiology quality improvement and patient safety, and a regular speaker regarding topics ranging from pediatric CT radiation dose optimization to radiologist peer review. He is the executive director of Stanford’s Realizing Improvement through Team Empowerment (RITE) program and co-director of the Clinical Effectiveness Leadership Training (CELT) program. He also leads the Stanford Medicine Improvement Capability Development Program.

Dr. Larson is the Founder and Program Chair for the Radiology Improvement Summit held at Stanford, now in its fourth year. He also serves on the Board of Trustees of the American Board of Radiology, overseeing quality and safety.

Prior to his position at Stanford, Dr. Larson was the Janet L. Strife Chair for Quality and Safety in Radiology and a faculty member of the James M. Anderson Center for Health Systems Excellence at Cincinnati Children’s Hospital in Cincinnati, Ohio. He holds MD and MBA degrees from Yale University and completed his training at the University of Colorado Health Sciences Center in Denver, Colorado. Dr. Larson is a pediatric radiologist at Lucile Packard Children's Hospital at Stanford. He and his wife, Tara, live in Portola Valley, California and have four children.


Michael T. Lu, MD

Michael T. Lu is Director of Research, Cardiovascular Imaging at Massachusetts General Hospital (MGH) and Assistant Professor of Radiology at Harvard Medical School. He completed his MD and MPH at Harvard, his Diagnostic Radiology residency at the University of California, San Francisco (UCSF), and fellowships in Thoracic and Cardiac Imaging at MGH.

As a practicing radiologist, Dr. Lu’s focus is improving health through imaging. Research interests include clinical trials of cardiac CT and deep learning to assess prognosis from medical imaging. He co-chairs the REPRIEVE Mechanistic CT Substudy, a multicenter randomized controlled trial of statins to reduce coronary plaque and cardiovascular events in HIV.


Koen Nieman, MD, PhD

Dr. Nieman is a cardiologist and Associate Professor in the departments of cardiovascular medicine and radiology at Stanford University, investigating advanced cardiac imaging techniques. His current projects include the development and technical validation of functional CT applications for ischemic heart disease and the clinical validation of cardiac CT in the form of clinical effectiveness trials. Dr. Nieman was born in the Netherlands, obtained his medical degree at the Radboud University in Nijmegen (1998) and completed his cardiology training at the Erasmus University Medical Center in Rotterdam (2008). His research in cardiac CT at the Erasmus University resulted in a PhD degree in 2003. In 2004, he performed an imaging fellowship at the Massachusetts General Hospital (Harvard Medical School) in Boston, MA. Dr. Nieman joined the staff of the department of cardiology and radiology at the Erasmus University Medical Center in 2008, where he was Scientific Director of the cardiac CT and MRI group and supervised the intensive cardiac care unit until he joined Stanford University in 2016.


Bhavik N. Patel, MD, MBA

Dr. Bhavik Patel is an assistant professor in the department of Radiology at Stanford University. He is the director of clinical trials and director of body CT. He is an active faculty member at Stanford’s Artificial Intelligence in Medical Imaging (AIMI) Center, leading a number of deep learning projects.


Pranav Rajpurkar, PhD

Pranav Rajpurkar is a 4th year PhD candidate in the Stanford Machine Learning Group co-advised by Professor Andrew Ng and Professor Percy Liang. He works on the development and deployment of deep learning algorithms for automated diagnosis, prognosis, and treatment of diseases. Pranav has developed models for automated detection of arrhythmias, multiple pathology detection under uncertainty for x-rays (CheXNet, MURA, CheXNeXt), and augmentation of experts in knee MRI interpretation (MRNet). Pranav has also developed SQuAD, a machine reading comprehension dataset.


Daniel L. Rubin, MD, MS

Dr. Rubin is a tenured Professor of Biomedical Data Science, Radiology, and Medicine (Biomedical Informatics Research) at Stanford University and Director of Biomedical Informatics for the Stanford Cancer Institute. His NIH-funded research program focuses on artificial intelligence (AI) in medicine and quantitative imaging for developing decision support applications that can personalize health care. His group develops AI applications in radiology, pathology, and ophthalmology for clinical diagnosis and prediction. He is a Fellow of the American College of Medical Informatics, the American Institute for Medical and Biological Engineering (AIMBE), and Distinguished Investigator in the Academy for Radiology & Biomedical Imaging. He has published over 250 peer-reviewed papers and has pending patents on 10 inventions.


Keith S. White, MD

Keith S. White MD, is the Medical Director of Imaging Services for Intermountain Healthcare. Dr. White is a graduate of the University Of Utah College Of Medicine. He completed a radiology residency at Duke University and a fellowship in pediatric radiology at Cincinnati Children’s Hospital. Dr. White has had a long-standing interest in imaging informatics, quality improvement and care process development. He oversees the integration of Intermountain’s 23 hospitals and seven radiology groups into an integrated imaging care delivery system. Areas of active focus include structured/synoptic reporting with integration of AI results into production imaging workflows.


Kristen Yeom, MD

Kristen Yeom is an Associate Professor of Radiology and faculty at the Center for Artificial Intelligence in Medicine and Imaging at Stanford University. She specialized in neuroradiology and serves as the interim director of Pediatric Neuroradiology and Associate Director of MRI at Lucile Packard Children’s Hospital at Stanford. She obtained her medical degree from University of Michigan, and did a diagnostic radiology residency at UCLA School of Medicine, and neuroradiology fellowship at Stanford University. Dr. Yeom’s research has focused on clinical and translational studies of advanced MRI methods, such as diffusion, perfusion and quantitative susceptibility MRI, as well as novel image processing tools for improved understanding of normal neural development and diagnosis and management of neurological and neuro-oncologic diseases. Her recent works include radiomic and machine-learning strategies for pediatric brain tumor classification, as well as computer vision tasks for clinical neuroimaging diagnostics, such as deep neural networks for assessing normal brain development and aging, and creation of deep vision classifier models for brain and neurovascular pathologies.