
One young cancer epidemiologist has received funding to conduct a long-term research project to explore whether differences in mammography screening are contributing to worse breast cancer outcomes for minority women in the U.S.
Dr. Justin Xavier Moore. Image courtesy of Kim Ratliff, Production Coordinator, Augusta University.In a previous project, Dr. Justin Xavier Moore identified hundreds of counties in the U.S. where Black and Hispanic women have high breast cancer death rates. Now the epidemiologist from the Medical College of Georgia at Augusta University is creating a new map to identify regions with low screening compliance.
The mammography map will combine data from the National Health Interview Survey and Behavioral Risk Factor Surveillance System in order to show low- and high-screening regions for Black and Hispanic women. Moore expects to find a lot of overlap between counties with low screening rates and high breast cancer death rates.
After making the map, Moore plans to survey 200 affected women from high-risk counties about factors that help or hinder their ability to access screening mammography. The hope is to identify trends that make some women, especially Black women, much more likely to be diagnosed with late-stage breast cancer and ultimately die from the disease.
In the end, Moore plans to sit down with 40 women to gain additional understanding. He hopes the project will eventually be a catalyst for a larger, community-based participatory research program aimed at screening and early breast cancer interventions for minority women.
The project is being funded by a $646,332 Mentored Research Scientist Career Development Award from the U.S. National Institute on Minority Health and Health Disparities. Dr. Steven Coughlin, from the Medical College of Georgia Department of Population Health Sciences, will serve as Moore's primary mentor.
![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)









