Until recently, the creation of artificial intelligence (AI) systems to assist radiologists and other medical imaging professionals have required “feature engineering”: the painstaking manual design of algorithms to process images, segment anatomic structures, detect features, and classify abnormalities. These systems often require years for computer scientists to create. Recent advances in artificial intelligence have replaced feature engineering with a more efficient process based on “deep” machine learning from massive sets of annotated images.
This presentation will provide a unique perspective on the implications of artificial intelligence and machine learning for medical imaging. We will review the motivations and origins of artificial intelligence in medical imaging, outline the fundamentals of neural networks, and describe several clinical examples of AI methods and their application. These methods are being developed for all phases of diagnostic imaging work flow, including for test selection, image enhancement, imaging triage, image quality control, computer-aided detection, computer-aided diagnosis, and improved communication. We will balance the AI hype by analysing key pitfalls of machine learning methods, and close with predictions for the likely future of AI in clinical imaging practice.
Curtis P. Langlotz MD, PhD. Professor of Radiology and Biomedical Informatics and Director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center) at Stanford University. 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.
The AIMI Center develops artificial intelligence methods that enable computer systems to draw precise and complex inferences directly from image information and associated clinical data, augmenting the skills of human imaging professionals. Dr. Langlotz has authored over 100 scholarly publications and the book, “The Radiology Report: A Guide to Thoughtful Communication for Radiologists and Other Medical Professionals”. He led the development of the RadLex standard terminology for radiology report information, a national standard for imaging exam codes, and a library of radiology report templates that have been downloaded over 5 million times. Dr. Langlotz is a past president of the Radiology Alliance for Health Services Research (RAHSR) and the Society for Imaging Informatics in Medicine (SIIM), and 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) and as president of the College of SIIM Fellows. Dr. Langlotz has founded 3 health care information technology companies, the most recent of which was acquired by Nuance Communications in 2016.
Dr Luke Oakden-Rayner Radiologist and medical researcher. He is currently undertaking a PhD at the University of Adelaide, where he develops artificial intelligence systems for use in medical imaging. He has lectured at the University of Adelaide and the University of Melbourne, and is regularly invited to speak about the intersection of medicine and artificial intelligence at conferences, on podcasts, and to the media. He is a passionate science communicator, and writes a popular academic blog on medical artificial intelligence and radiology at lukeoakdenrayner.wordpress.com He can also be found on Twitter @drlukeor and Reddit /u/drlukeor
HISA webinars have been approved for up to 1 contact hour of continuing education credit toward renewal of the CHIA credential. Click here for more information on becoming a certified health informatics professional.