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Dr. Langlotz is Professor of Radiology and Biomedical Informatics and Director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center), which supports outstanding interdisciplinary artificial intelligence research that optimizes how clinical images are used to promote health. As Associate Chair for Information Systems and a Medical Informatics Director for Stanford Health Care, he is also responsible for the computer technology that supports the Stanford Radiology practice, including 7 million imaging studies that occupy 0.7 petabytes of storage. Dr. Langlotz’s laboratory investigates the use of deep neural networks and other machine learning technologies to help radiologists detect disease and eliminate diagnostic errors. 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. He has published over 100 scholarly articles, and is author of the recent book “The Radiology Report: A Guide to Thoughtful Communication for Radiologists and Other Medical Professionals”. Raised in St. Paul, Minnesota, Dr. Langlotz received his undergraduate degree in Human Biology, masters 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 serves on the Board of Directors of the Radiological Society of North America (RSNA) as Liaison for Information Technology. He 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 has founded three healthcare information technology companies, most recently 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.
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