Dear Women's Imaging Insider,
At the recent RSNA 2014 meeting, much of the discussion about women's imaging focused on digital breast tomosynthesis (DBT) -- particularly on how it reduces recall rates.
In this first Women's Imaging Insider of 2015, we're highlighting two such studies from the meeting. One explored whether radiologists' experience affects recall rates in tomosynthesis studies, while the other investigated whether DBT influences recall rates in women undergoing baseline breast cancer screening. To read the full article, click here.
When you've finished our Insider Exclusive, take a look at what else is going on in the Women's Imaging Community:
- Read why Mayo Clinic researchers are excited about molecular breast imaging for dense tissue screening.
- Check out why Canadian researchers believe that race and ethnicity influence the likelihood of women receiving an early diagnosis of breast cancer -- the reason may not be what you'd expect.
- Learn how computer-aided detection for breast CT can be improved.
- Find out how the use of DBT could slash healthcare costs.
- Discover how many of California's primary care physicians are unaware of the state's breast density notification law and what this might suggest about the legislation.
As always, if you have a comment, report, or article idea to share about any aspect of women's imaging, please contact me.

![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)









