The debate over annual versus biennial breast cancer screening continues as the American College of Physicians (ACP) rolls out its latest guidance statement.
The ACP's guidance calls for biennial screening for women ages 50 to 74 who are at average risk, while women ages 40 to 49 years should engage in informed discussions with their clinicians.
The new guidance, intended for internal medicine professionals, also addresses supplemental imaging, with the ACP saying clinicians should consider digital breast tomosynthesis (DBT) for women with heterogeneously or extremely dense breasts (BI-RADS C or D).
Carolyn Crandall, MD, joins this week's episode to discuss the new guidance. Crandall is a professor of medicine at the University of California, Los Angeles (UCLA) and chair of ACP’s Clinical Guidelines Committee.
Carolyn Crandall, MD, from the ACP and UCLA discusses the issue of annual versus biennial breast cancer screening in the wake of new guidance by the ACP, which calls for biennial screening for women ages 50 to 74 who are at average risk.
Crandall shares how the ACP came to issue its guidance, including what data was used when developing the college's statement. She also discusses considerations that women and clinicians should have when engaging in informed decision making for breast cancer screening.
Finally, Crandall addresses the debate between annual and biennial screening and what the ACP will monitor as the new guidance is issued.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)




![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)









