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

Rhea-AI Summary

DeepHealth (Nasdaq: RDNT) launched Breast Suite on Dec 1, 2025, an AI-powered, modular platform for breast cancer screening, detection, risk stratification and workflow.

Key clinical validations include a 21% increase in cancer detection across >579,000 women, 23% more cancers detected in dense breasts, and 20% more cancers detected in Black, non-Hispanic women. The suite supports >10 million mammograms annually and integrates detection, automated density, a mammogram-calibrated risk model (reported as 2x accuracy vs questionnaire models), cloud viewing, prioritized worklists and intelligent reporting.

Positive

• Cancer detection +21% in 579,000-woman real-world study
• Dense-breast detection +23% versus prior practice
• Detection +20% in Black, non-Hispanic women
• Risk model ~2x accuracy vs questionnaire-based models
• Platform deployed across >10 million mammograms annually

Negative

• Breast arterial calcification (BAC) assessment is currently in development

News Market Reaction

-3.36%

1 alert

-3.36% News Effect

On the day this news was published, RDNT declined 3.36%, reflecting a moderate negative market reaction.

Data tracked by StockTitan Argus on the day of publication.

Key Figures

Screening volume: >10 million mammograms annually US study size: 579,000 women Detection increase: 21% increase +5 more

8 metrics

Screening volume >10 million mammograms annually Breast Suite-supported annual screening volume

US study size 579,000 women Real-world AI breast screening analysis across 100+ sites

Detection increase 21% increase Breast cancer detection rate in US Nature Health analysis

Dense breast detection 23% more cancers Women with dense breasts in US analysis

Black, non-Hispanic detection 20% more cancers Black, non-Hispanic women in US analysis

European study size 154,000 women Science Translational Medicine risk assessment study

High-risk cohort 10% of women Highest-risk group identified by AI risk model

Earlier cancer detection 44% of cancers Cancers potentially detected earlier with AI-based supplemental screening

Market Reality Check

Price: $73.17 Vol: Volume 989,968 vs 20-day ...

normal vol

$73.17 Last Close

Volume Volume 989,968 vs 20-day average 862,927 (relative volume 1.15x). normal

Technical Price 78.66 trading above 200-day MA at 63.47, near 52-week high 85.84.

Peers on Argus

RDNT gained 1.69% while key peers were mixed: GH -2.86%, SHC +0.30%, CRL -0.26%,...

RDNT gained 1.69% while key peers were mixed: GH -2.86%, SHC +0.30%, CRL -0.26%, RVTY +0.38%, WGS -1.80%, suggesting a company-specific move around the DeepHealth Breast Suite launch.

Historical Context

5 past events · Latest: Dec 01 (Positive)

Pattern 5 events

Date	Event	Sentiment	Move	Catalyst
Dec 01	AI product launch	Positive	-3.4%	Launch of DeepHealth Breast Suite AI platform for breast screening workflows.
Nov 20	Customer win	Positive	-1.3%	Wichita Radiological Group adopts DeepHealth Operations Suite under five-year deal.
Nov 17	Clinical study data	Positive	+2.8%	ASSURE study in Nature Health shows higher cancer detection and predictive value.
Nov 12	Strategic collaboration	Positive	-1.3%	Expanded GE HealthCare collaboration to scale AI imaging across modalities and regions.
Nov 11	Acquisition announcement	Positive	-2.9%	Acquisition of CIMAR UK to strengthen DeepHealth’s cloud AI imaging footprint.

Pattern Detected

Recent AI and digital health announcements have mostly seen negative next-day reactions despite positive clinical and strategic content, with only the Nov 17 Nature Health study aligning positively.

Recent Company History

Over the last month, RadNet’s DeepHealth unit has reported multiple AI-driven milestones. On Nov 11, RadNet acquired CIMAR UK to expand cloud imaging in the UK and Europe. A Nov 12 collaboration with GE HealthCare broadened AI imaging across modalities. On Nov 17, a Nature Health study showed a 21.6% cancer detection increase, drawing a positive reaction. Subsequent workflow and customer wins, plus today’s Breast Suite launch supporting >10 million mammograms annually, were followed by mostly negative price moves, indicating a tendency to sell into strong AI news.

Market Pulse Summary

This announcement highlights DeepHealth’s Breast Suite, an AI-powered platform already supporting ov...

Analysis

This announcement highlights DeepHealth’s Breast Suite, an AI-powered platform already supporting over 10 million mammograms annually and backed by data showing a 21% increase in cancer detection, including benefits for dense-breast and diverse populations. Recent history shows RadNet repeatedly investing in AI through acquisitions, collaborations, and large real-world studies. Investors may watch how adoption, clinical performance, and integration with existing workflows evolve relative to these prior initiatives and to RadNet’s broader imaging network.

Key Terms

breast arterial calcification, mammograms, screening mammography, multistage AI-driven workflow, +1 more

5 terms

breast arterial calcification medical

"Breast Arterial Calcification (BAC) assessment:4 Currently in development, BAC is intended"

Deposits of calcium that build up in the small arteries of the breast, typically seen on mammogram images, indicate hardened or stiff blood vessels much like mineral buildup inside household pipes. For investors, this finding matters because it is a noninvasive marker of broader cardiovascular disease risk and can influence demand for diagnostic services, cardiovascular treatments, imaging technologies, and insurance and healthcare spending trends.

mammograms medical

"mammograms from over 579,000 women across 100+ community-based imaging sites"

A mammogram is a specialized X‑ray image of the breast used to detect early signs of cancer or other abnormalities, much like a high-resolution camera that looks beneath the skin. For investors, mammograms matter because screening guidelines, diagnostic accuracy, and reimbursement rules directly influence demand for imaging machines, medical services, and related software, which can affect revenue and regulatory risk for healthcare providers and device makers.

screening mammography medical

"Reporting Tool for Screening Mammography: Real-World Evidence of Workflow Impact"

Screening mammography is a routine X-ray exam of the breasts used to detect early signs of cancer in people who have no symptoms, similar to a preventive car inspection that looks for hidden problems before they become obvious. It matters to investors because changes in screening guidelines, technology, insurance coverage, or public uptake directly affect demand for imaging equipment, clinic revenues, diagnostic services and related healthcare companies, influencing sales, reimbursement and long-term growth prospects.

multistage AI-driven workflow technical

"A Real-World Deployment of a Multistage AI-Driven Workflow in Breast Screening"

A multistage AI-driven workflow is a sequence of connected steps where artificial intelligence performs different tasks at each stage — like an assembly line where smart machines inspect, sort, and refine work as it moves along. For investors, it signals greater automation and efficiency that can lower costs, speed time-to-market, and scale operations, while also concentrating risk around data quality, model performance and ongoing maintenance.

double reading medical

"reduce workloads compared to traditional double reading."

Double reading is a quality-control process where two independent experts review the same medical image, test result, or data point separately to confirm findings and reduce mistakes. For investors, double reading matters because it strengthens the reliability of diagnostic claims and clinical-trial outcomes, lowering the chance of costly errors or regulatory setbacks—like having two proofreaders check an important report before it's published.

AI-generated analysis. Not financial advice.

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 diagnosis^1,6 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.

FAQ

What did DeepHealth (RDNT) announce on Dec 1, 2025 about Breast Suite?

DeepHealth announced Breast Suite, an AI-driven, modular platform for detection, risk stratification and workflow across breast screening and diagnostics.

How much did Breast Suite improve cancer detection in the reported study?

A real-world analysis reported a 21% increase in breast cancer detection across >579,000 women.

Does Breast Suite improve detection for dense breasts and Black patients (RDNT)?

Yes — the study reported 23% more cancers detected in women with dense breasts and 20% more in Black, non-Hispanic women.

What risk assessment capability does DeepHealth Breast Suite offer and how accurate is it?

An AI risk model estimates 1–2 year breast cancer risk from a mammogram and was reported as about 2x more accurate than questionnaire-based models.

How does Breast Suite affect radiology workflow for providers using RDNT solutions?

The suite includes cloud multi-modality viewing, prioritized worklists, rapid alerts and intelligent reporting to speed reads and standardize reports.

Is the Breast Arterial Calcification (BAC) feature available in Breast Suite?

No — BAC assessment is noted as currently in development and not yet released.