| Integration of Expert Systems in Clinical Radiology: NIH Perspective |
| Ronald Summers |
| July 22, 2020, Wednesday, 3:00 PM - 4:00 PM EDT |
| Abstract There has been explosive growth recently in research on artificial intelligence in radiology. This growth is attributable to advances made in machine learning by researchers in the fields of computer vision and medical image analysis. The potential clinical applications of artificial intelligence in radiology are many, including fully-automated screening for cancer, organ analysis, and quantitative imaging. The potential benefits to the patient are also many, including a reduction in diagnostic errors and interobserver variability, dissemination of expert knowledge through algorithms trained on relevant populations, and timelier image interpretation. This presentation will highlight these applications and benefits, and will attempt to put these novel developments in artificial intelligence in radiology in perspective. |
| Bio Dr. Summers received a BA in physics and the M.D. and Ph.D. degrees in medicine/anatomy and cell biology from the University of Pennsylvania. He completed a medical internship at the Presbyterian-University of Pennsylvania Hospital, Philadelphia, PA, a radiology residency at the University of Michigan, Ann Arbor, MI, and an MRI fellowship at Duke University. In 2000, he received the Presidential Early Career Award for Scientists and Engineers, presented by Dr. Neal Lane, President Clinton's science advisor. In 2012, he received the NIH Director's Award, presented by NIH Director Dr. Francis S. Collins. He is an editorial board member of the journals Radiology and Academic Radiology. He was a co-chair of the Computer-aided Diagnosis program of the annual SPIE Medical Imaging conference in 2010 and 2011. He has co-authored over 300 journal, review and conference proceedings articles, and is a co-inventor on 12 patents. |