Abstract
The ideal practice routine for screening mammography would optimize performance metrics and minimize costs, while also maximizing patient satisfaction. The main approaches to screening mammography interpretation include batch offline, non-batch offline, interrupted online, and uninterrupted online reading, each of which has its own advantages and drawbacks. This article reviews the current literature on approaches to screening mammography interpretation, potential effects of newer technologies, and promising artificial intelligence resources that could improve workflow efficiency in the future.
Abstract
Challenges currently facing breast radiologists, including controversial screening mammography guidelines, radiologist burnout, and the perceived threat posed by artificial intelligence could deter potential candidates from pursuing a career in radiology. However, breast radiologists play a fulfilling role by decreasing the effect of breast cancer through both early detection and direct interaction with patients and interdisciplinary clinical colleagues. While perception is that artificial intelligence will threaten the need for radiologists, it is more likely that it will improve image interpretation and efficiency in workflow, thereby further improving patient care. Trainees can be engaged in breast imaging through interactive teaching methods and by role modeling clinical and image interpretation skills.
Radiology Implementation Considerations for Artificial Intelligence (AI) Applied to COVID-19, From the AJR Special Series on AI Applications
