Expanding Horizons: The Realities of CAD, the Promise of Artificial Intelligence, and Machine Learning’s Role in Breast Imaging beyond Screening Mammography

Abstract

Artificial intelligence (AI) applications in mammography have gained significant popular attention; however, AI has the potential to revolutionize other aspects of breast imaging beyond simple lesion detection. AI has the potential to enhance risk assessment by combining conventional factors with imaging and improve lesion detection through a comparison with prior studies and considerations of symmetry. It also holds promise in ultrasound analysis and automated whole breast ultrasound, areas marked by unique challenges. AI’s potential utility also extends to administrative tasks such as MQSA compliance, scheduling, and protocoling, which can reduce the radiologists’ workload. However, adoption in breast imaging faces limitations in terms of data quality and standardization, generalizability, benchmarking performance, and integration into clinical workflows. Developing methods for radiologists to interpret AI decisions, and understanding patient perspectives to build trust in AI results, will be key future endeavors, with the ultimate aim of fostering more efficient radiology practices and better patient care.

Operating Artificial Intelligence to Assist Physicians Diagnose Medical Images: A Narrative Review

“…AI-powered solutions may lessen inter-observer variability and steer radiologists to suspicious areas of interest, which can improve diagnosis accuracy. Prioritisation of mammography cases for additional screening can be improved using artificial intelligence methods [10]. AI-based solutions can aid the triage process by detecting lesions or anomalies of concern, flagging cases that need further investigation, and helping prioritise patients based on the potential severity of their condition.…”