AI-Enhanced Mammograms Reduce False Negative Breast Cancer Screening Results

Mammograms, a tool used to detect breast cancer in women commonly age 50 years or older, have been used for more than 100 years, with modern and more frequent use dating back anywhere from 50 to 60 years. Now, with the evolution of modern technology and AI, mammograms have evolved to increase clarity and provide more detailed images and analysis of results.

SimonMed, one of the largest outpatient medical imaging providers in the US, recently introduced 2 AI-enhanced mammogram services: Mammogram+ and Mammogram+Heart, which both aim to improve early detection and refine preventative medicine.¹ Computer-aided detection (CAD) used during mammogram screenings dates to the 1990s, when the ImageChecker M1000 created by R2 Technology became the first FDA-approved CAD system for mammograms in 1998.²

AI-enhanced mammograms can potentially improve early detection. | Image Credit: Gorodenkoff - stock.adobe.com .jpeg

“At SimonMed Imaging, we’re focused on empowering women with the latest technology to take charge of their health,” said John Simon, MD, CEO and founder of SimonMed Imaging, in a press release. “Mammogram+ and Mammogram+ Heart combine advanced AI to detect breast cancer earlier and identify early signs of heart disease—offering valuable insight into both breast and heart health through a single exam.”

The Mammogram+ and Mammogram+Heart utilize Profound AI, an AI-powered mammography detection software from the company iCAD. The company has multiple versions of its mammography detection software, 2 of which are FDA-cleared: Profound Detection V4 and ProFound AI V3 both support radiologists with overall diagnostic accuracy and performance readings for 2D and 3D mammography and digital breast tomosynthesis, which have been shown to improve the accuracy of hard-to-find cancers by 22% and reduce false positives by 18%.²

ProFound AI was trained on over 20 million images, gathering data for more than 130 sites globally, and uses advanced neural network image processing and pattern recognition to analyze for mass, distortion, calcification, and asymmetry.

iCAD also has early detection software, which has not been FDA-cleared but is CE marked and Health Canada licensed. This model is used to predict breast cancer risk during annual mammogram screening up to 2 years in advance and has proven to be 2.4 times more accurate when compared to more traditional models like the Gail and Tyrer-Cuzick models.³

SimonMed also utilizes iCAD’s breast arterial calcifications (BAC) within mammograms and recognizes that it is a risk for cardiovascular disease (CVD) even amongst women who are not considered high-risk (i.e., women without diabetes, hypertension, hyperlipidemia, chronic kidney disease, or known CVD). Furthermore, in patients that are high risk, BAC can help to refine risk assessment. For example, in women with diabetes, the presence of BAC was associated with a 2.5 increased risk of cardiovascular events or all-cause death.²

“Women deserve the best tools available when it comes to detecting disease early,” said Dr. Angela Fried, Director of Breast Imaging at SimonMed, in a press release. “With Mammogram+ and Mammogram+ Heart, we’re offering more than a scan—we’re delivering deeper insight, earlier intervention, and more empowered care.”

References:

1. The science of using AI to detect breast cancer. News release. iCAD. April 17, 2025. Accessed July 14, 2025. https://www.icadmed.com/about/science/.

2. Resch D, Gullo RL, Teuwn J, et al, AI-enhanced mammography with digital breast tomosynthesis for breast cancer detection: clinical value and comparison with human performance. 2024; (6)4. doi:10.1148/rycan.230149

3. Saccenti L, Jedida BB, Minssen L, et al. Evaluation of a deep learning-based software to automatically detect and quantify breast arterial calcifications on digital mammogram. 2025; (106)3:98-104. doi:10.1016/j.diii.2024.10.001