RadNet’s DeepHealth and HOPPR Forge Partnership to Advance AI in Healthcare

Rhea-AI Summary

DeepHealth, a RadNet subsidiary, has partnered with HOPPR to advance AI in healthcare. The collaboration aims to commercialize a Medical-Grade Generalized Foundational Model and develop Fine-Tuned models for breast, prostate, and lung cancer detection. This partnership leverages HOPPR's medical-grade infrastructure and DeepHealth's clinical expertise to enhance diagnostic accuracy and workflow efficiency in radiology.

Key points:

• DeepHealth's cloud-native operating system integrates clinical and operational tools
• HOPPR's model enhances medical research and simplifies data collection and training
• The collaboration aims to accelerate AI development in medical imaging
• DeepHealth's technology is used in over 800 clinical sites, performing 15 million exams annually

Positive

• Partnership with HOPPR to develop advanced AI models for cancer detection
• Potential to enhance diagnostic accuracy and speed up image analysis
• DeepHealth's technology used in over 800 clinical sites
• AI tools perform over 15 million exams annually
• Over 2 million AI-informed diagnoses made

Negative

• None.

AI and Healthcare Technology Expert

This partnership between DeepHealth and HOPPR marks a significant advancement in AI-driven healthcare. The collaboration aims to develop a Medical-Grade Generalized Foundational Model, which could revolutionize medical imaging analysis. This model has the potential to enhance diagnostic accuracy and accelerate image analysis, particularly for breast, prostate and lung cancer detection.

The integration of HOPPR's foundational model with DeepHealth's clinical expertise could lead to more efficient and accurate AI tools for radiology. This could result in faster diagnoses, improved patient outcomes and streamlined workflows for healthcare providers. The partnership also highlights the growing trend of AI integration in healthcare systems, potentially reducing costs and improving overall efficiency in medical imaging.

Financial Analyst

This partnership could significantly impact RadNet's (NASDAQ: RDNT) market position and financial performance. By leveraging HOPPR's AI technology, RadNet's subsidiary DeepHealth may be able to accelerate its product development and expand its market reach. The collaboration could lead to new revenue streams from advanced AI-powered diagnostic tools and potentially increase RadNet's competitive edge in the medical imaging market.

Investors should note that while this partnership shows promise, the development and commercialization of AI models in healthcare often face regulatory hurdles and require substantial investment. The long-term financial benefits may take time to materialize, but if successful, this could position RadNet as a leader in AI-driven medical imaging, potentially driving future growth and shareholder value.

Medical Research Analyst

This collaboration between DeepHealth and HOPPR represents a significant step forward in medical research and clinical practice. The development of a Medical-Grade Generalized Foundational Model could accelerate hypothesis generation and streamline data collection for various medical studies. This has the potential to reduce research costs and expedite the discovery process across multiple medical fields.

The focus on fine-tuned models for breast, prostate and lung cancer detection is particularly noteworthy. These areas often require complex imaging analysis and improved AI tools could lead to earlier detection and more accurate diagnoses. However, it's important to monitor how these AI models perform in real-world clinical settings and ensure they meet rigorous regulatory standards before widespread adoption.

CAMBRIDGE, Mass., Sept. 05, 2024 (GLOBE NEWSWIRE) -- DeepHealth, a wholly-owned subsidiary of RadNet, Inc. (NASDAQ: RDNT) and a global leader in AI-powered radiology and health informatics, today announces a data and AI development partnership with HOPPR (www.hoppr.ai). This collaboration will commercialize a pioneering Medical-Grade Generalized Foundational Model and foster the development of Fine-Tuned models for breast, prostate, and lung cancer detection, leveraging generative medical imaging-focused AI and robust, diverse data sets.

HOPPR’s Medical-Grade Generalized Foundation Model enhances medical research and hypotheses while simplifying and lowering costs for data collection and training. AI Foundational Models are versatile, pre-trained architectures that serve as a starting point for customizing specific tasks through Fine-Tuned models, for which expertise in a particular domain is critical. The partnership seeks to create new Fine-Tuned models, powered by HOPPR’s Medical-Grade Generalized Foundation Model, to bolster DeepHealth’s AI-powered health informatics portfolio by enabling it to create future solutions more quickly and efficiently and to support the evolution of radiology in the coming years. DeepHealth’s cloud-native operating system (OS) is designed to integrate clinical and operational tools, to provide radiology workflow efficiencies and improve patient outcomes.

Sham Sokka, PhD, Chief Operating and Technology Officer, DeepHealth, said, “DeepHealth’s partnership with HOPPR is a significant leap forward in DeepHealth’s mission to empower breakthroughs in care through enabling new diagnostic imaging technologies.”

“The integration of Foundational Models like those being developed by HOPPR in medical imaging is intended to boost diagnostic accuracy, speed up image analysis, and pave the way for generative AI in non-clinical applications, including workflow automation, ultimately enhancing patient care and outcomes in radiology. At DeepHealth, we are not just a provider of AI technology but are creating a comprehensive portfolio of solutions for medical imaging, seamlessly blending AI-based automation and efficiencies into an operating system for radiology and diagnostic workflows,” added Mr. Sokka.

HOPPR’s robust medical-grade infrastructure and tools for accelerating AI and machine learning development, combined with DeepHealth’s deep clinical expertise and successful track record in deploying AI tools at scale and in real-world settings, aim to unlock significant diagnostic, clinical, and operational value from medical imaging data and advance imaging across modalities.

“We are pleased to partner with DeepHealth to transform healthcare informatics,” said Khan Siddiqui, MD – Chief Executive Officer of HOPPR. “Our collaboration on Medical-Grade Foundation Models and infrastructure supporting them could significantly enhance medical imaging, leveraging AI’s transformative potential to improve clinical care efficiency and quality. HOPPR is collaborating with DeepHealth to build a unified clinical and operational workflow that enables radiologists to efficiently access the information they need through the systems they know.”

DeepHealth’s unique 'one system' approach addresses challenges across the entire radiology value chain, from referral management, scheduling, and patient engagement to technologist and radiologist workflows. DeepHealth OS supports radiology departments with a comprehensive solution for medical imaging, including operational solutions and end-to-end services across the care continuum.

DeepHealth and other RadNet Digital Health technology is used in over 800 clinical sites in select countries, and its AI tools perform over fifteen million exams annually, resulting in more than two million AI-informed diagnoses.

About DeepHealth
DeepHealth, a wholly-owned subsidiary of RadNet, Inc. (NASDAQ: RDNT), provides AI-powered health informatics to empower breakthroughs in care delivery. The heart of its portfolio of solutions, the DeepHealth OS, is a cloud-native operating system that orchestrates all clinical and operational data to drive value across the enterprise. The portfolio builds on the strengths of RadNet’s existing digital health businesses and products, including eRAD Radiology Information Systems and Image Management Systems, Aidence lung AI, Quantib prostate AI, and DeepHealth breast AI. DeepHealth aims to elevate the role of the radiologist beyond radiology and across the entire care pathway. It empowers all users across the care continuum with personalized workflows to make work easier and more meaningful. DeepHealth leverages advanced AI operational and clinical technologies in breast, lung, brain, and prostate health, leading to increased operational efficiency, clinical confidence, and patient outcomes. https://deephealth.com/

About HOPPR AI
HOPPR is transforming medical imaging by providing medical-grade foundation models and infrastructure that enables real-time engagement with data and integration with clinical systems that enable physicians, technicians, and clinical support staff to “converse” with medical imaging studies, changing medical imaging interactions from static to dynamic. HOPPR has created both medical and administrative use cases that it will unveil with commercial partners at RSNA in December 2024. https://hoppr.ai/

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 the DeepHealth health informatics product portfolio, the DeepHealth OS and each’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, please contact:

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

FAQ

What is the purpose of the partnership between DeepHealth and HOPPR?

The partnership aims to commercialize a Medical-Grade Generalized Foundational Model and develop Fine-Tuned models for breast, prostate, and lung cancer detection, leveraging generative medical imaging-focused AI and robust, diverse data sets.

How many clinical sites use DeepHealth's technology?

DeepHealth's technology is used in over 800 clinical sites in select countries.

How many exams do DeepHealth's AI tools perform annually?

DeepHealth's AI tools perform over 15 million exams annually, resulting in more than 2 million AI-informed diagnoses.

What is DeepHealth's approach to radiology workflow?

DeepHealth uses a 'one system' approach that addresses challenges across the entire radiology value chain, from referral management and scheduling to technologist and radiologist workflows.

How might this partnership affect RadNet's (RDNT) stock?

While the direct impact on RadNet's (RDNT) stock is not specified, the partnership could potentially enhance RadNet's AI capabilities in medical imaging, which may positively influence investor sentiment and future business prospects.