Computer-aided detection (CAD) is a promising technology that is changing the way many breast imaging facilities interpret mammograms. But CAD still needs some fine-tuning, in particular to address a high false-positive rate that leaves mammographers relying heavily on biopsy to determine whether CAD-flagged areas really are malignant.
Dear AuntMinnie Member,
Computer-aided detection (CAD) is a promising technology that is changing the way many breast imaging facilities interpret mammograms. But CAD still needs some fine-tuning, in particular to address a high false-positive rate that leaves mammographers relying heavily on biopsy to determine whether CAD-flagged areas really are malignant.
Several articles we’re featuring this week in our Women’s Imaging Digital Community deal with CAD in general, and more specifically with the false-positive dilemma. Our first article, by staff writer Shalmali Pal, tracks the efforts by a joint team from the U.S and Israel to analyze the way CAD reviews microcalcifications to see whether a new data-analysis algorithm could provide high sensitivity with a lower biopsy rate.
Another study, in the same article, tracks the use of a CAD-based lesion-analysis program to improve the characterization of suspicious areas in the BI-RADS 3 category, a tricky gray area that includes lesions that are probably benign but that require follow-up anyway. The CAD application was able to correctly upgrade suspicious lesions to a more serious BI-RADS category with 90% sensitivity, according to the authors.
Finally, in a report from the just-concluded European Congress of Radiology, staff writer Eric Barnes describes the experience German researchers had in test-driving a new version of one of the leading CAD software programs. While sensitivity was only slightly improved, they found that the program was effective in reducing the false-positive rate significantly.
Get all the details in our Women’s Imaging Digital Community, at womens.auntminnie.com.


![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)







