DeepHealth Introduces New AI-Powered Radiology Informatics and Population Screening Solutions at #ECR2025

Rhea-AI Summary

DeepHealth, a subsidiary of RadNet (NASDAQ: RDNT), will showcase new AI-powered radiology solutions at ECR 2025 in Vienna. The company will introduce Diagnostic Suite™, a cloud-native PACS alternative, and SmartMammo™, an AI-powered mammography solution that has shown a 21% increase in cancer detection rate.

The company's AI-powered Lung solution is currently supporting the NHS England's Lung Cancer Screening Program, where 76% of cancers were detected at earlier stages, compared to 29% historically. Their Prostate solution has demonstrated 97% diagnostic sensitivity and 37% reduction in workflow time for MRI reads.

DeepHealth's solutions are powered by DeepHealth OS, a cloud-native operating system integrating data across various platforms. The company's technology is currently utilized by over 800 clinical sites and more than 3,000 radiologists globally.

Positive

• 21% increase in cancer detection rate with SmartMammo™
• 76% early-stage cancer detection rate in NHS lung screening program
• 97% diagnostic sensitivity in prostate cancer detection
• 37% reduction in MRI workflow time
• Large-scale adoption: 800+ clinical sites and 3,000+ radiologists

Negative

• None.

Insights

Healthcare Technology Analyst

RadNet's DeepHealth subsidiary is strategically expanding its AI-powered diagnostic imaging portfolio at ECR 2025, signaling an acceleration of the company's transformation from a traditional imaging center operator to a technology-driven healthcare solutions provider. This product expansion represents a significant opportunity to diversify RadNet's revenue streams beyond its core imaging services business.

The new Diagnostic Suite and enhanced SmartMammo solutions address critical pain points in radiology workflows while generating compelling clinical outcomes. The reported 21% increase in cancer detection rates and 37-42% workflow time reductions represent quantifiable value propositions that could drive adoption among European healthcare providers facing radiologist shortages and increasing imaging volumes.

What's particularly notable is RadNet's execution of a platform strategy through DeepHealth OS - creating an ecosystem that can more easily scale across different imaging modalities and clinical applications. This approach mirrors successful SaaS business models in other industries, potentially generating high-margin recurring revenue streams that could significantly improve RadNet's profitability profile over time.

The NHS England implementation provides a important reference case for European market penetration, demonstrating real-world validation in one of the continent's largest healthcare systems. This positions RadNet favorably against both established medical imaging incumbents and newer AI-focused startups that lack comparable deployment scale.

For investors, this expansion represents both near and long-term value drivers. Near-term, these solutions can enhance the efficiency of RadNet's own network of imaging centers, improving margins on existing operations. Longer-term, successful commercialization of these AI solutions could position RadNet as a significant healthcare technology player, potentially commanding higher valuation multiples more aligned with healthcare SaaS companies than traditional imaging service providers.

The strategic partnerships with TeraRecon and CARPL.ai suggest a pragmatic approach to market expansion, leveraging established distribution channels rather than building everything in-house - a strategy that could accelerate European market penetration while managing development costs.

Medical Technology Specialist

RadNet's DeepHealth is strategically positioning itself at the intersection of two critical healthcare trends: the global radiologist shortage and the explosive growth in diagnostic imaging volumes. Their ECR 2025 announcements reveal a comprehensive approach to embedding AI directly into clinical workflows rather than offering standalone detection tools - addressing a key adoption barrier that has the impact of many first-generation radiology AI solutions.

The clinical validation data is particularly compelling. The 21% increase in cancer detection rates for mammography and 76% early-stage detection for lung cancer represent meaningful clinical improvements that translate directly to patient outcomes. These aren't merely incremental gains; they represent potential paradigm shifts in population health screening effectiveness.

What distinguishes DeepHealth's approach is their focus on end-to-end workflow integration through DeepHealth OS. Unlike point solutions that create additional steps for radiologists, these tools are designed to reduce cognitive load and streamline interpretation. The reported 37-42% workflow time reductions could allow radiologists to handle significantly higher volumes without sacrificing quality - a critical consideration as imaging centers face staffing challenges and growing backlogs.

The NHS England implementation provides important real-world validation in a value-based care system where cost-effectiveness is rigorously evaluated. This suggests the technology can meet the stringent health technology assessment criteria typical in European healthcare systems, potentially accelerating adoption across similar nationalized health services.

For European radiology departments considering these solutions, the key differentiator appears to be DeepHealth's holistic approach spanning multiple modalities and clinical applications. Rather than requiring integration of various vendor-specific AI tools, the platform offers a unified ecosystem that can potentially reduce IT complexity and training requirements.

The timing of this European market push is significant, coinciding with the implementation phase of the EU's AI Act and Medical Device Regulation, which creates both challenges and opportunities. RadNet appears well-positioned with clinical validation data that will likely be necessary to navigate the increasingly stringent European regulatory environment for AI-based medical devices.

• At ECR 2025, DeepHealth will introduce new AI-powered radiology informatics and cancer screening solutions enabled by DeepHealth OS, its pioneering cloud-native operating system, to address clinical and operational challenges.
• DeepHealth aims to bring to European customers its Diagnostic Suite™ to redefine the traditional Picture Archiving and Communication System (PACS) with a unified workspace tailored for modern radiology, and SmartMammo™, an AI-powered end-to-end diagnostic SaaS solution for mammography, further embedding AI-based detection and diagnosis in interpretive workflows.¹
• DeepHealth will also showcase updates to its broader population health portfolio of clinical AI solutions supporting lung, prostate, and brain health.
• To extend and scale these solutions, DeepHealth leverages strategic collaborations with leading industry players, such as enterprise imaging solutions provider TeraRecon and AI infrastructure company CARPL.ai.

VIENNA, Feb. 26, 2025 (GLOBE NEWSWIRE) -- DeepHealth, Inc., a global leader in AI-powered health informatics and a wholly-owned subsidiary of RadNet, Inc. (Nasdaq: RDNT), will introduce next-generation AI-powered radiology informatics and population screening solutions at the European Congress of Radiology (ECR) 2025 in Vienna. With the aim of empowering breakthroughs in care enabled by imaging, DeepHealth’s suite of solutions addresses critical clinical and operational challenges experienced by healthcare providers around the world. DeepHealth’s solutions are powered by DeepHealth OS, a secure, scalable, and cloud-native operating system that integrates data across various solutions and enables interoperability with broader healthcare IT ecosystems. Moreover, to enable AI adoption at scale, DeepHealth has established strategic collaborations enabling integrated solutions with ecosystem industry leaders.

“At DeepHealth, we are harnessing the transformative power of AI to create cutting-edge solutions that are deeply rooted in real-world clinical needs,” said Kees Wesdorp, PhD, President and CEO of RadNet’s Digital Health division. “We believe that AI adoption at scale in radiology can only be achieved by embedding AI into and unifying data across clinical and operational workflows to empower radiologists and care teams that today are having to rely on disconnected devices and disparate data sources. At ECR 2025, we are excited to showcase our next-generation radiology informatics and population screening solutions designed to enhance operational efficiency, clinical confidence, and care delivery across the radiology continuum.”

Streamlining workflows with next-generation radiology informatics solutions
DeepHealth aims to bring to European customers its latest innovations that harness the power of AI to streamline operations, support higher volumes of patients, and assist radiologists and care teams in diagnosing patients faster and with greater accuracy. 

At ECR 2025, DeepHealth will showcase Diagnostic Suite™, which harnesses the cloud-native capabilities of DeepHealth OS to set a new benchmark in medical imaging technology and the traditional Picture Archiving and Communication System (PACS). Diagnostic Suite delivers a unified AI-powered interpretive experience that is intended to seamlessly integrate with or replace existing RIS/PACS systems, bringing advanced capabilities to meet the evolving demands of radiologists. A unified application, the diagnostic workspace brings together worklist, patient brief, reporting, and high-speed streaming viewer technologies with advanced visualization and AI orchestration capabilities. Diagnostic Suite is cloud-native, enabling high reliability and scalability to practices of all sizes and scale.

DeepHealth is also announcing upcoming updates to SmartMammo™, a comprehensive suite of advanced tools designed to optimize breast cancer screening and diagnosis for more efficient workflows, which will support 2D mammograms, further improving local workflow integration. DeepHealth’s 3D solution is compatible with leading OEMs and integrates seamlessly with radiologist worklists and reporting tools. It features clinical AI detection capabilities that automatically localize lesions and assess suspicion levels across diverse populations and dense breast tissue, enabling a 21% increase in cancer detection rate.² The updates will incorporate already CE-marked 2D technology to assist in the primary read and fulfill the second reader role in mammography double reading workflows.³

State-of-the-art clinical AI solutions for lung, prostate, and brain health
DeepHealth will also showcase its latest clinical results and feature updates to its broader population health portfolio. DeepHealth’s clinical AI solutions include comprehensive, end-to-end AI-powered solutions for clinical diagnostics and high-volume screening in lung, breast, and prostate health, along with AI-driven innovations for brain health. DeepHealth’s solutions are powering screening programs for breast cancer in the US and lung cancer in Europe.

DeepHealth’s AI-powered Lung solution⁴ is used to assist radiologists in the NHS England’s Lung Cancer Screening Program, one of the world’s largest lung cancer screening initiatives. Within this program, 76% of detected cancers were caught at earlier, more treatable stages, compared to only 29% historically (without AI).⁵ The solution provides AI-powered nodule detection, reporting, and patient management for enhanced efficiency and accuracy in lung cancer diagnosis by automatically detecting and segmenting lung nodules, enabling radiologists to interpret lung CT scans more confidently and, according to a recent study, up to 42% faster.⁶   

In prostate cancer, DeepHealth’s AI-powered Prostate solution⁷ supports high-volume population health screening programs, diagnostic interpretation, and biopsy planning enabled by automated prostate segmentation, standardized reporting, and integration with most fusion biopsy systems. The solution has been shown to enable a 97% diagnostic sensitivity, up from 92% for radiologists without AI support⁸ and recent analyses revealed a 37% reduction in workflow time for MRI read.⁹

Elements of DeepHealth’s portfolio are already delivering meaningful impact for patients and healthcare providers around the world, with over 800 clinical sites and more than 3,000 radiologists leveraging its current RIS, PACS, and AI solutions to deliver better care.

At ECR 2025, DeepHealth’s portfolio of solutions¹ is presented at Booth no. 507, X5.

About RadNet, Inc.
RadNet, Inc. is the leading national 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 399 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 affiliated radiologists, and inclusive of full-time and per diem employees and technologists, RadNet has a total of over 10,000 employees. For more information, visit www.radnet.com.

About DeepHealth
DeepHealth is a wholly-owned subsidiary of RadNet, Inc. (NASDAQ: RDNT) and serves as the umbrella brand for all companies within RadNet's Digital Health division. DeepHealth provides AI-powered health informatics with the aim of empowering breakthroughs in care through imaging. Building on the strengths of the companies it has integrated and is rebranding (i.e., eRAD Radiology Information and Image Management Systems and Picture Archiving and Communication System, Aidence lung AI, DeepHealth and Kheiron breast AI and Quantib prostate and brain AI), DeepHealth leverages advanced AI for operational efficiency and improved clinical outcomes in lung, breast, prostate, and brain 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 radiologists at hundreds 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/

Forward Looking Statement
This press release contains “forward-looking statements” within the meaning of the safe harbor provisions of the U.S. Private Securities Litigation Reform Act of 1995. Forward-looking statements, including statements regarding the capabilities of RadNet, CARPL.ai, TeraRecon, and DeepHealth’s informatics, hardware and software product portfolios and the collaboration’s impact on radiology practices and healthcare workflow, are expressions of our current beliefs, expectations, and assumptions regarding the future of our business, future plans and strategies, projections, and anticipated future conditions, events and trends. Forward-looking statements can generally be identified by words such as: “anticipate,” “intend,” “plan,” “goal,” “seek,” “believe,” “project,” “estimate,” “expect,” “strategy,” “future,” “likely,” “may,” “should,” “will” and similar references to future periods.

Forward-looking statements are neither historical facts nor assurances of future performance. Because forward-looking statements relate to the future, they are inherently subject to uncertainties, risks and changes in circumstances that are difficult to predict and many of which are outside of our control. Our actual results and financial condition may differ materially from those indicated in the forward-looking statements. Therefore, you should not place undue reliance on any of these forward-looking statements.

For media inquiries, reach out to:

DeepHealth
Andra Axente
Communications Director
Phone: +31 614 440971
Email: andra.axente@deephealth.com

RadNet, Inc.
Mark Stolper
Executive Vice President and Chief Financial Officer
310-445-2800

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¹ Not all products are regulatory cleared for use in all markets. Regulatory clearance varies by region and product. Please refer to www.deephealth.com or contact our regulatory affairs team for more information on the specific clearances applicable in your market.
² SmartMammo Dx + Assure used in a safeguard review process improved cancer detection rate by 21% in a study of over 575,000 patients, showing the same benefits across racial subgroups and patients with different breast densities. Data on file.
³ Not all products are regulatory cleared for use in all markets. Regulatory clearance varies by region and product. Please refer to www.deephealth.com or contact our regulatory affairs team for more information on the specific clearances applicable in your market.
⁴  DeepHealth’s lung solution (Veye Lung Nodules in Europe) is manufactured for DeepHealth by Aidence B.V.
⁵ UK Department of Health and Social Care. New lung cancer screening roll out to detect cancer sooner, 2023.
⁶ Hempel HL et al. Higher agreement between readers with deep learning CAD for reporting pulmonary nodules on CT. Eur J Radiol Open. 2022;9:100435. doi: 10.1016/j.ejro.2022.100435.
⁷ DeepHealth’s prostate solution is manufactured as Quantib® Prostate for DeepHealth by Quantib B.V.
⁸ Günzel et al., 2024.
⁹ Data on file.

FAQ

What is the cancer detection improvement rate for DeepHealth's SmartMammo™ solution?

SmartMammo™ enables a 21% increase in cancer detection rate across diverse populations and dense breast tissue.

How many clinical sites currently use DeepHealth's RDNT solutions?

Over 800 clinical sites and more than 3,000 radiologists currently use DeepHealth's RIS, PACS, and AI solutions.

What are the early detection results of DeepHealth's AI Lung solution in the NHS program?

76% of cancers were detected at earlier, more treatable stages, compared to only 29% historically without AI.

What efficiency improvements does RDNT's AI Prostate solution provide?

The solution achieves 97% diagnostic sensitivity and reduces MRI workflow time by 37%.