Support teaching, research, and patient care.
Dr. Langlotz is Professor of Radiology, Medicine, and Biomedical Data Science at Stanford University. His laboratory investigates the use of deep neural networks and other machine learning technologies to detect disease and eliminate diagnostic errors through analysis of medical images and clinical notes. He also serves as Associate Director of Stanford’s Institute for Human-Centered Artificial Intelligence and as Director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center), which supports over 150 Stanford faculty conducting interdisciplinary artificial intelligence research that optimizes how clinical data are used to promote health. As Associate Chair for Information Systems and a Medical Informatics Director for Stanford Health Care, Dr. Langlotz is responsible for the computer technology that supports the Stanford Radiology practice, including 8 million imaging studies that occupy nearly a petabyte of storage. He has published over 150 scholarly articles and is author of the book “The Radiology Report: A Guide to Thoughtful Communication for Radiologists and Other Medical Professionals”. He has led many national and international efforts to improve the quality of radiology communication, including the RadLex™ terminology standard, the RadLex™ Playbook of radiology exam codes, the RSNA report template library, and a technical standard for communication of radiology templates. Raised in St. Paul, Minnesota, Dr. Langlotz received his undergraduate degree in Human Biology, Master’s in Computer Science, MD in Medicine, and PhD in Medical Information Science, all from Stanford University. He is a founder and past president of the Radiology Alliance for Health Services Research (RAHSR) and has served as president of the Society for Imaging Informatics in Medicine (SIIM), and the College of SIIM Fellows. He is a former board member of the Association of University Radiologists (AUR), the American Medical Informatics Association (AMIA) and the Society for Medical Decision Making (SMDM). He currently chairs the Board of Directors of the Radiological Society of North America (RSNA). Dr. Langlotz is a recipient of the Lee B. Lusted Research Prize from the Society of Medical Decision Making and the Career Achievement Award from the Radiology Alliance for Health Services Research. He and his trainees have received numerous scientific awards, including seven best paper awards and five research career development grants. He has founded several healthcare information technology companies, including Montage Healthcare Solutions, which was acquired by Nuance Communications in 2016.
My laboratory employs deep neural networks and other machine learning technologies to design algorithms that detect and classify disease on medical images. We also develop natural language processing methods that use narrative radiology reports to create large annotated image training sets for supervised machine learning experiments. The resulting systems provide real-time decision support for radiologists to improve accuracy and reduce errors. We are committed to enabling the clinical use of ideas conceived in the laboratory. When our results show potential, we evaluate their utility in the reading room or the clinic and disseminate them as open source or commercial software.
Validation of an Artificial Intelligence-based Algorithm for Skeletal Age Assessment
The purpose of this study is to understand the effects of using an Artificial Intelligence
algorithm for skeletal age estimation as a computer-aided diagnosis (CADx) system. In this
prospective real-time study, the investigators will send de-identified hand radiographs to
the Artificial Intelligence algorithm and surface the output of this algorithm to the
radiologist, who will incorporate this information with their normal workflows to make an
estimation of the bone age. All radiologists involved in the study will be trained to
recognize the surfaced prediction to be the output of the Artificial Intelligence algorithm.
The radiologists' diagnosis will be final and considered independent to the output of the
Stanford is currently not accepting patients for this trial.
For more information, please contact Safwan Halabi, M.D., (650) 721-2850.
View full details