Title: AI in Clinical Practice
Speaker: Nina Kottler, MD, MS, Radiologist, VP Clinical Operations, Radiology Partners, El Segundo, CA
The Department of Medical Imaging is pleased to have Nina Kottler, MD, MS, presenting at our Grand Rounds on Wednesday, March 18th at 12:00 pm in the College of Medicine, Room 2117.
Dr. Kottler is a radiologist with over 14 years of experience in emergency radiology. With a graduate degree in applied mathematics and optimization theory, she has been using imaging informatics to improve quality and drive value in radiology. Dr. Kottler is a VP of Clinical Operations at Radiology Partners, leading their Data Science and Analytics division. She also developed and practices in Radiology Partners’ remote imaging division and serves internally on their AI, IT and Culture & Leadership support boards. Externally, Dr. Kottler recently served on the ACR Informatics Commission and serves on the following committees: ACR Metrics Committee, ACR Quality and Safety Conference Planning Committee, SIIM Machine Learning Committee, SIIM Program Committee (Scientific Abstract Reviewer), RSNA Educational Exhibits Committee (Radiology Informatics Subcommittee), and the RADxx Steering Committee. In 2018, she received the Trailblazer Award – an award recognizing a pioneering female leader in the field of imaging informatics.
Abstract: There has been a huge amount of venture capital infused into Artificial Intelligence (AI) and with that an explosion in the number of AI vendors in healthcare. At its peak some AI experts were suggesting that AI would replace radiologists. However, despite billions of dollars in spending, deployment of AI algorithms has been limited and even fewer companies are being compensated for use of their algorithms. We will use Gartner’s Hype Cycle to review the maturity of AI in Radiology and hypothesize what will happen in the next couple of years. We will discuss several reasons why AI is not commonly seen in clinical use including the need for large volumes of high-quality data and bias. We will use the basic principles of optimization theory to construct a framework to identify use cases for AI applications and review several AI applications in clinical use.