Dear Women's Imaging Insider,
In July, the U.S. Department of Health and Human Services (HHS) issued guidelines that require new private health plans to cover preventive services, including mammography screening for women 40 and older. And they can't charge co-pays for these services, according to HHS.
The combination of these two factors could boost mammography screening rates. Read our article on the topic by clicking here.
Once you've finished with that story, take a look at what else is going on in the Women's Imaging Digital Community:
- Discover the American Society for Radiation Oncology's guidelines for the use of hypofractionated whole-breast radiation therapy treatment.
- Learn about a breast imaging technique using contrast with subharmonic ultrasound.
- Check out what researchers are saying about a less invasive option for dealing with persistent fibroids.
- Find out what researchers discovered about conebeam breast CT's imaging scope and radiation dose.
- Get the scoop: Double readers top CAD in finding breast tissue deformities.
- Discover why sonography in women with abnormal uterine bleeding is easier to perform when gel is used to distend the uterus rather than saline.
- Read why young women have the highest cancer risk from body CT scans.
As always, if you have a comment, report, or article idea to share about any aspect of women's imaging, I invite you to contact me at [email protected].
![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&dpr=2&fit=crop&h=167&q=70&w=250)
