Bibb Allen, MD FACR
Chief Medical Officer ACR Data Science Institute
Diagnostic Radiologist

Dr. Allen is a diagnostic radiologist with the Birmingham Radiological Group and is in community practice at Grandview Medical Center in Birmingham, Alabama. He is Chief Medical Officer of the ACR Data Science Institute and a past president of the American College of Radiology. He is a former chair of the ACR Board of Chancellors and also has extensive experience in the socioeconomic issues of radiology as they relate to coding, the valuation of physician work and practice expense. He is a strong proponent of the ACR’s Imaging 3.0 initiative to ensure radiologists hold a key role in evolving health care delivery and payment models, as well as quality patient care. Dr. Allen earned his undergraduate degree from the University of Alabama and his medical degree from the University of Alabama School of Medicine. He did his residency training at the University of Alabama Hospitals and the Baptist Health System in Birmingham, Alabama and abdominal imaging fellowship at Georgetown University Hospital in Washington, DC. He has been active with the ACR for over 20 years and has served as a Councilor, member of the Council Steering Committee, Chair of the Carrier Advisory Committee (CAC) Network, ACR representative to the AMA/Specialty Society RVS Update Committee (the RUC) and Chair of the Commission on Economics.

Evaluating And Monitoring Artificial Intelligence For Clinical Practice: Is FDA Clearance Enough?

The pace of regulatory clearance of artificial intelligence algorithms continues to accelerate, and in diagnostic radiology numerous AI algorithms are becoming available for use in clinical practice. However, end-users of AI in radiology should be aware that AI algorithms may not work as expected when used beyond the institutions where they were trained, and model performance may degrade over time. In this presentation the reasons why regulatory clearance alone may not be enough to ensure AI will be safe and effective in all radiological practices will be reviewed and strategies available resources for evaluating prior to clinical use and monitoring performance of AI models to ensure efficacy and patient safety will be discussed.