Dec. 01, 2025 — DeepHealth, a wholly owned subsidiary of RadNet, Inc., has launched the DeepHealth Breast Suite,2 an end-to-end suite of modular, interoperable AI-powered applications that address real-world clinical needs across the breast cancer screening and detection pathways. Breast Suite builds on organic innovation and integrated technologies from iCAD to deliver a comprehensive suite of solutions.
The Suite brings together AI-powered breast cancer detection, breast density assessment, risk assessment3 and in-development breast arterial calcification4 with cloud-first viewing, reporting and workflow tools to accelerate interpretation and diagnostic workflow. Today, components of Breast Suite enhance diagnostic accuracy1 and standardization of care5 across more than 10 million mammograms annually.
“The launch of Breast Suite marks a pivotal step toward a new, AI-powered standard of care in breast cancer screening and diagnostic pathways,” said Kees Wesdorp, President and CEO of RadNet’s Digital Health Division, DeepHealth. “By embedding detection and risk intelligence with workflow tools, we give radiologists more capabilities to detect cancers earlier, with more confidence and to elevate patient care.”
Advancing Early Detection and Enhanced Diagnostic Accuracy
Breast Suite integrates a broad set of clinical AI applications, including the following:
- ProFound Pro, leading AI-powered cancer detection: Enables more accurate diagnosis1,6 with the use of prior data,7 automatic localization of regions of interest and degree of suspicion.
- Automated density assessment: Provides consistent, automated density classification with a patient-centric, accurate density assessment of 2D or 3D mammograms to support objective diagnosis decisions.
- AI-powered risk assessment:3 Identifies risk of developing breast cancer in 1-2 years, based only on a mammogram calibration, with 2x greater accuracy than traditional questionnaire-based risk models.8,9
- Breast Arterial Calcification (BAC) assessment:4 Currently in development, BAC is intended to reveal cardiovascular disease risk by automatically flagging breast arterial calcifications on screening mammograms.
These capabilities have been validated in large-scale clinical studies. Recently published in Nature Health, the largest real-world analysis of AI-powered breast cancer screening in the US on mammograms from more than 579,000 women across 100+ community-based imaging sites, demonstrated that DeepHealth Breast Suite applications enabled a 21% increase in breast cancer detection rate.1 The study showed consistent benefits across dense-breast and diverse patient populations, including 23% more cancers detected in women with dense breasts and 20% more cancers detected in Black, non-Hispanic women.1 Furthermore, the technology has been proven to raise the performance of generalist radiologists to the level of specialists, expanding access to high-quality breast care in regions where experienced readers may be limited.10
In a separate Science Translational Medicine study of 154,000 women in Europe, DeepHealth’s AI-powered risk assessment model was found to accurately estimate short-term breast cancer risk based on age, breast density and mammographic features. Researchers estimated that if the 10% of women at highest risk had been offered supplemental screening based on the AI assessment, up to 44% of the cancers could have potentially been detected earlier, compared to 20% using Tyrer-Cuzick traditional risk models.11
Together, these results underscore the technology’s ability to enhance detection, guide individualized risk-stratified screening pathways, and support more equitable and effective breast cancer care.
Improving Workflow Consistency and Reviewer Performance
Breast Suite extends beyond clinical AI capabilities to incorporate workflow tools that elevate radiologist performance and enhance operational efficiency:
- Cloud-first multi-modality Viewer:12 Enables multi-modality image viewing, including MRI and ultrasound, in addition to mammograms, to provide a comprehensive reading solution across the breast care pathway, accessible from anywhere.
- Prioritized worklist: Creates efficient workflows, prioritizing cases by suspicion level and processing large volumes of data without delays.
- Timely alerts: Improves turnaround time with rapid image processing that flags high suspicion cases within minutes13 and enables care teams to provide same-day follow-ups.
- AI-powered Safeguard Review workflow: Improves cancer detection rate with second reviewer workflow, decreasing false negatives and emphasizing likely missed cancers, including hard-to-detect ones.1,14,15
- Intelligent reporting: Improves clinical consistency through customizable reporting with guideline standardization and automatic pre-population of breast density findings.
Built on DeepHealth’s OS, Breast Suite applications integrate seamlessly with existing customer technology, offer secure, fast remote access and ensure a unified, standardized clinical experience. With continuous updates and rapid scaling, Breast Suite evolves alongside clinical needs.
Learn more at https://deephealth.com.
References
1. Louis, L. et al. “Equitable Impact of an AI-Driven Breast Cancer Screening Workflow in Real World US-wide Deployment.” Nature Health, 2025.
2. Breast Suite comprises multiple applications including ProFound Pro, ProFound AI, Breast Density, Safeguard Review, Risk Assessment, and DeepHealth Viewer. DeepHealth Viewer is manufactured by eRAD, Inc. and distributed by DeepHealth, Inc. Risk Assessment is not cleared for use in the U.S. BAC is in development; regulatory submission planned prior to the end of 2025. Not cleared for use in the US. Not all products and functions are available in all markets. Any claims made about Breast Suite may reference claims associated with its individual components.
3. Not cleared for use in the U.S. Capability available in Europe.
4. In development, regulatory submission planned prior to the end of 2025. Not cleared for use in the US.
5. McCabe et al. “Multistage AI-Driven Workflow Improves General Radiologist Screening Mammography Performance to the Level of Fellowship-Trained Breast Imagers: Real-world Evidence in >500,000 Patients.” RSNA Chicago. 2025.
6. FDA 510(k) clearance K251873. Clinical Performance Testing.
7. FDA 510K Pending.
8. Mikael Eriksson et al., A risk model for digital breast tomosynthesis to predict breast cancer and guide clinical care. Sci. Transl. Med. 14, eabn3971 (2022). DOI:10.1126/scitranslmed.abn3971.
9. Eriksson et al. “Identification of Women at High Risk of Breast Cancer Who Need Supplemental Screening.” Radiology. Sep 2020.
10. Kim et al., Radiol Artif Intell., 2024.
11. Mikael Eriksson et al., A risk model for digital breast tomosynthesis to predict breast cancer and guide clinical care. Sci. Transl. Med. 14, eabn3971 (2022). DOI:10.1126/scitranslmed.abn3971.
12. Optional multimodality viewer for new exams from Ultrasound and MRI.
13. Rapid image processing flags highly suspicious cases in under 5 minutes when integrated with GE HealthCare’s Senographe Pristina system using 1 GB bandwidth transmission, and under 15 minutes with HOLOGIC.
14. Louis et al. “Large-scale deployment of a multistage AI-driven workflow increases detection of deadlier breast cancers.” RSNA Chicago. 2025.
15. McCabe et al. “Multistage AI-Driven Workflow Improves General Radiologist Screening Mammography Performance to the Level of Fellowship-Trained Breast Imagers: Real-world Evidence in >500,000 Patients.” RSNA Chicago. 2025.
16. Not all products and functionalities are commercially available in all countries. For clearance and commercial availability in your geography and compatibility with other systems, please contact a DeepHealth representative.
December 08, 2025 
