Manisha Bahl, MD, MPH, is a radiologist at the Massachusetts General Hospital (MGH) and Harvard Medical School and a past Director of the MGH Breast Imaging Fellowship Program. She is a graduate of Stanford University, the UC-San Francisco School of Medicine, and the Harvard School of Public Health. She completed residency and fellowship training at Duke University Medical Center and, most recently, completed the Professional Certificate in Machine Learning and Artificial Intelligence at the Massachusetts Institute of Technology. Dr. Bahl is an NIH-funded investigator whose research interests include the application of artificial intelligence to improve outcomes in women with breast cancer and the clinical assessment of digital breast tomosynthesis. She has won numerous awards for her research, including the ARRS President’s Award, and has served on study sections for the NIH and Department of Defense.
Abstract: Artificial intelligence (AI) is a branch of computer science dedicated to developing computer algorithms that imitate intelligent human behavior. Subfields of AI include machine learning and deep learning. Advances in AI have led to techniques that could increase breast cancer detection, improve clinical efficiency, and guide decision-making with regard to screening and prevention strategies. This presentation will review key terminology and concepts, discuss common AI models and methods to validate and evaluate these models, describe emerging AI applications in breast imaging, and outline challenges and future directions.
Zoom meeting: https://arizona.zoom.us/j/83801133505
- Review key terminology and concepts in artificial intelligence
- Discuss methods to validate and evaluate artificial intelligence models
- Describe artificial intelligence applications in breast imaging
The University of Arizona College of Medicine - Tucson is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.
The University of Arizona College of Medicine - Tucson designates this live activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)ä. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
All Faculty, CME Planning Committee Members, and the CME Office Reviewers have disclosed that they have no financial relationships with commercial interests that would constitute a conflict of interest concerning this CME activity.