DeepHealth Launches Breast Suite, Elevating Breast Cancer Detection, Risk Stratification and Workflow

GlobeNewswire | RadNet, Inc.
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Breast Suite is an industry-leading, comprehensive, and modular AI-powered suite of applications supporting more than 10 million mammograms annually delivering increased breast cancer detection rates,1 risk stratification tools, and viewing and reporting workflow acceleration

CHICAGO, Dec. 01, 2025 (GLOBE NEWSWIRE) -- DeepHealth, a global leader in AI-powered health informatics and a wholly owned subsidiary of RadNet, Inc. (Nasdaq: RDNT), announced today the launch of the DeepHealth Breast Suite,2 a first-of-its-kind 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 new suite of solutions. The Suite brings together industry-leading 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.”

The Breast Suite embodies DeepHealth’s mission of empowering breakthroughs in care through imaging, demonstrating how AI-powered solutions can advance population health by stage shifting disease, driving more timely and effective screening and diagnostic pathways, and expanding access to meaningful innovation. 

Stage Shift Disease: 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 over 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. 

Optimized Diagnostics: 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.

Research Presentations at RSNA 2025
DeepHealth will present the following research abstracts at the annual meeting, reflecting the capabilities of Breast Suite:

Improving Cancer Detection

  • Increasing Cancer Detection in Dense Breasts: A Real-World Deployment of a Multistage AI-Driven Workflow in Breast Screening with Stratified Analyses on Over 570,000 Cases
    Dec. 1 from 3-4 p.m. CST, Podium, S406A
    This research finds that DeepHealth's AI-powered breast cancer screening solution increases cancer detection rates across varying breast density groups — including for women with dense breast tissue, which makes it more difficult to detect tumors.

Elevating Radiologists’ Performance

  • 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
    Dec. 2 from 9:30-10:30 a.m. CST, Podium, S406A
    This study demonstrates how DeepHealth's AI-powered, multistage breast cancer screening solution enables general radiologists to achieve performance levels comparable to fellowship-trained breast imaging specialists.

  • Leveraging the Diagnostic Complementarity Between AI and Human Reading to Reach Superior Outcomes in Breast Screening
    Dec. 4 from 12:45-1:15 p.m. CST, Poster, Learning Center
    Featuring DeepHealth’s breast AI technology, this abstract explains how combining AI with human review can improve accuracy and reduce workloads compared to traditional double reading.

Improving Operational Efficiency 

  • Radiologist-Industry Collaboration in Developing and Deploying an Efficient Clickable Reporting Tool for Screening Mammography: Real-World Evidence of Workflow Impact
    Nov. 30 1:15-1:45 p.m. CST, Podium, Learning Center Theater 1
    Using DeepHealth’s intelligent reporting technology, this study highlights how close collaboration between radiologists and engineers leads to measurable reductions in read times and improved reporting efficiency.

At RSNA 2025, DeepHealth’s Breast Suite and broader portfolio of solutions16 is presented at Booth #1329, South Hall, Level 3 at McCormick Place in Chicago.

About DeepHealth 
DeepHealth is a wholly owned subsidiary of RadNet, Inc. (NASDAQ: RDNT) and serves as the umbrella brand for RadNet’s Digital Health segment. DeepHealth provides AI-powered health informatics with the aim of empowering breakthroughs in care through imaging. DeepHealth leverages advanced AI for operational efficiency and improved clinical outcomes in breast, chest, prostate, neuro, and thyroid health. At the heart of DeepHealth’s portfolio is a cloud-native operating system – DeepHealth OS – that unifies data across the clinical and operational workflow and personalizes AI-powered workspaces for everyone in the radiology continuum. Thousands of imaging centers and radiology departments around the world use DeepHealth solutions to enable earlier, more reliable, and more efficient disease detection, including in large-scale cancer screening programs. DeepHealth’s human-centered, intuitive technology aims to push the boundaries of what’s possible in healthcare. https://deephealth.com 

About RadNet, Inc. 
RadNet, Inc. is a leading provider of freestanding, fixed-site diagnostic imaging services in the United States based on the number of locations and annual imaging revenue. RadNet has a network of 407 owned and/or operated outpatient imaging centers. RadNet’s markets include Arizona, California, Delaware, Florida, Maryland, New Jersey, New York and Texas. In addition, RadNet provides radiology information technology and artificial intelligence solutions marketed under the DeepHealth brand, teleradiology professional services and other related products and services to customers in the diagnostic imaging industry. Together with contracted radiologists, and inclusive of full-time and per diem employees and technologists, RadNet has over 11,000 team members. https://radnet.com

Forward-Looking Statements
This communication contains certain “forward-looking statements” within the meaning of the safe harbour provisions of the U.S. Private Securities Litigation Reform Act of 1995, Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. Forward-looking statements can be identified by words such as: “anticipate,” “believe,” “could,” “estimate,” “expect,” “forecast,” “intend,” “may,” “outlook,” “plan,” “potential,” “possible,” “predict,” “project,” “seek,” “should,” “target,” “will” or “would,” the negative of these words, and similar references to future periods. Examples of forward-looking statements include statements regarding the unifying clinical and operational intelligence into one system and enabling rapid-scale infrastructure that accelerates adoption, our technology becomes a catalyst to stage shift disease, expand patient access, elevate care teams and enhance operational efficiency, discussions regarding our product feature, and statements regarding our recent acquisitions. Actual results could differ materially from those currently anticipated due to a number of risks and uncertainties, many of which are beyond RadNet’s control.

Forward-looking statements are neither historical facts nor assurances of future performance. Instead, they are based only on management’s current beliefs, expectations and assumptions regarding the future of RadNet’s business, future plans and strategies, projections, anticipated events and trends, the economy and other future conditions. Because forward-looking statements relate to the future, they are subject to inherent uncertainties, risks and changes in circumstances that are difficult to predict and many of which are outside of RadNet’s control. RadNet’s actual results and financial condition may differ materially from those indicated in the forward-looking statements as a result of various factors. Neither RadNet, nor any of its directors, executive officers, or advisors, provide any representation, assurance or guarantee that the occurrence of the events expressed or implied in any forward-looking statements will actually occur, or if any of them do occur, what impact they will have on the business, results of operations or financial condition of RadNet. Should any risks and uncertainties develop into actual events, these developments could have a material adverse effect on RadNet’s business and the ability to realize the expected benefits of the acquisition. Risks and uncertainties that could cause results to differ from expectations include, but are not limited to: (1) the ability to recognize the anticipated benefits of the technology, and (2) the risk of legislative, regulatory, economic, competitive, and technological changes, and other risks and uncertainties described in the “Risk Factors,” “Management’s Discussion and Analysis,” and other sections of our filings with the Securities and Exchange Commission, including our most recent Annual Report on Form 10K and Quarterly Reports on Form 10Q. The foregoing review of important factors should not be construed as exhaustive and should be read in conjunction with the other cautionary statements that are included elsewhere. Additional information concerning risks, uncertainties and assumptions can be found in RadNet’s filings with the Securities and Exchange Commission (the “SEC”), including the risk factors discussed in RadNet’s most recent Annual Report on Form 10-K, as updated by its Quarterly Reports on Form 10-Q and future filings with the SEC.

Forward-looking statements included herein are made only as of the date hereof and, except as required by applicable law, RadNet does not undertake any obligation to update any forward-looking statements, or any other information in this communication, as a result of new information, future developments or otherwise, or to correct any inaccuracies or omissions in them which become apparent. All forward-looking statements in this communication are qualified in their entirety by this cautionary statement.

DeepHealth Media Contact
Andra Axente
Director of Communications
+31614440971
andra.axente@deephealth.com

RadNet Media Contacts
Jane Mazur
SVP, Corporate Communications
+1 585-355-5978
jane.mazur@radnet.com

Mark Stolper
Executive Vice President and Chief Financial Officer
+1 310-445-2800

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 suspicion cases in under 5 minutes when integrated with GE HealthCare’s Senographe Pristina system and 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 of functionalities listed and compatibility with other systems, please contact a DeepHealth representative.

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