DeepHealth
Contact for pricing
Regulatory Status Disclosed
Overview
DeepHealth, a wholly owned subsidiary of RadNet, Inc., is a cloud-native health informatics and clinical AI platform designed to optimize radiology workflows and enhance diagnostic precision across clinical and operational environments . The platform is engineered for radiologists, imaging center operators, and large-scale population health screening programs .
DeepHealth integrates clinical AI directly into the diagnostic workspace, unifying image management, viewing, and reporting into a single ecosystem . Its core offerings include:
- Diagnostic and Operations Suites: Cloud-first workspaces that combine PACS and RIS functionalities, streamlining patient scheduling, billing, and image interpretation .
- Modality-Specific AI Suites: Targeted clinical tools for breast (mammography), chest (lung nodule detection), prostate (MRI segmentation and biopsy planning), neuro (brain volume and longitudinal tracking), and thyroid imaging .
- AI Studio: An orchestration engine that integrates over 140 proprietary and third-party AI algorithms directly into the radiologist’s worklist and viewer, complete with performance monitoring and governance tools .
- Reporting Pro: An AI-assisted reporting solution that automates structured report generation by pulling clinical measurements and findings directly into draft impressions .
By embedding these capabilities into the daily clinical workflow, DeepHealth aims to reduce manual documentation, standardize reporting, and support high-volume diagnostic demands .
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-powered breast cancer detection (Breast Suite, Saige-Dxu2122)
- AI-powered prostate cancer detection (Prostate Suite)
- AI-powered lung nodule detection and reporting (Chest Suite)
- AI-powered neuroimaging analysis for neurodegenerative changes (Neuro Suite)
- AI-powered thyroid nodule detection and reporting (Thyroid Suite)
- AI-powered radiology reporting (Reporting Pro)
- Cloud-native operating system (DeepHealth OS)
- Remote scanning and collaboration platform (TechLiveu2122)
- Automated breast density assessment
- Prioritization of high-suspicion cases
Use Cases
- Enhancing workflow and diagnostic accuracy in breast cancer detection and screening programs.
- Improving prostate cancer detection and interpretation, compatible with fusion biopsy systems.
- Assisting radiologists in nodule detection, reporting, and patient management for lung cancer screening.
- Automating and standardizing neuroimaging analysis for conditions like Alzheimer's disease.
- Streamlining radiology reporting with AI-generated findings, speech recognition, and structured reporting.
- Enabling remote acquisition and supervision of multi-modality imaging procedures to address staffing shortages and expand access.
What Physicians Need to Know
Evidence Base (guidelines, literature scope)
DeepHealth's AI solutions are built upon extensive evidence, including deep learning algorithms trained with lung cancer screening cohorts for nodule detection, classification, quantification, and growth. Their Breast Suite applications have been validated in large-scale clinical studies, including a real-world analysis of over 579,000 women across 100+ community-based imaging sites. DeepHealth's solutions support various guidelines, such as ACR TI-RADS for thyroid nodule assessment and PI-RADS-compliant reporting for prostate. They also support programs like NHS England's Lung Cancer Screening program and France's CASCADE lung cancer screening study.
Clinical Validation Studies
DeepHealth has conducted numerous clinical validation studies. A Nature Health study demonstrated that DeepHealth Breast Suite applications led to a 21% increase in breast cancer detection rate, with consistent benefits across diverse patient populations, including a 23% increase in women with dense breasts and a 20% increase in Black, non-Hispanic women. For lung nodule detection, their AI significantly increased radiologists' detection of actionable nodules with minimal increase in false positives. In prostate cancer, their AI assistance has the potential to reduce missed clinically significant cancer instances from 8% to 1% and can elevate a radiologist-in-training's diagnostic sensitivity and positive predictive value to that of an expert. Real-world deployment of Prostate AI detected 27% more lesions and reduced inter-radiologist segmentation variability by 65%. LumbarMR has shown up to 94% sensitivity and an average 17% reduction in reporting time. Neuro Suite solutions have demonstrated 92% sensitivity in early identification of hippocampal atrophy associated with mild cognitive disorders and Alzheimer's disease.
Alert Fatigue Management
While not explicitly detailed for DeepHealth, the broader context of AI in healthcare acknowledges alert fatigue as a significant issue. DeepHealth's focus on integrating AI findings directly into structured reports and streamlining workflows aims to reduce manual data transfer and potentially mitigate alert fatigue by providing clinically relevant results efficiently.
Override Rate Data
For DeepHealth's AI-based measurements and characterization, radiologists accepted them without correction in greater than 94% of cases in real-world deployments across over 200 RadNet sites.
Drug Interaction Checking
DeepHealth's primary focus is on AI-powered imaging diagnostics and informatics. There is no information available indicating that DeepHealth currently provides drug interaction checking capabilities. This is a distinct area of clinical decision support.
Differential Diagnosis Support
DeepHealth's AI solutions provide support for differential diagnosis by enhancing detection and characterization across various imaging modalities. For example, in breast cancer, the AI highlights suspicious lesions and provides risk assessment. For lung nodules, it assists with detection, quantification, growth assessment, and classification. In prostate, it offers automated lesion detection and risk classification. For neurodegenerative changes, it provides automated quantification of brain structures and white matter hyperintensities.
Guideline Update Frequency
DeepHealth's Breast Suite offers intelligent reporting with guideline standardization, and its solutions are continuously updated and scaled alongside clinical needs. The company emphasizes evolving its portfolio to meet new standards of AI-powered care.
Clinical Workflow Integration
DeepHealth's solutions are designed for seamless integration into existing clinical workflows. The DeepHealth OS is a cloud-native operating system that unifies data across clinical and operational workflows and personalizes AI-powered workspaces. Their Diagnostic Suite unifies viewing, advanced visualization, structured reporting, and intelligent worklist management. Reporting Pro integrates clinical AI findings and measurements directly into the reporting workflow, supporting speech recognition, AI-generated impressions, and structured reporting. TechLiveu2122 offers a multi-modality, remote acquisition and collaboration platform.
Decision Audit Trail
While DeepHealth emphasizes transparency and auditability in its AI-powered processes, particularly in the context of employment decision tools, specific details about a clinical decision audit trail for physicians are not explicitly provided. However, the integration of AI findings into structured reports and the emphasis on a unified operating system suggest a framework for tracking AI contributions to diagnoses.
Physician Tip
Leverage DeepHealth's AI for enhanced detection and characterization in breast, lung, prostate, neuro, and thyroid imaging to improve diagnostic accuracy and efficiency. Utilize the integrated reporting features to streamline documentation and ensure guideline adherence. Embrace the AI-powered workflow to potentially reduce reading times and focus on complex cases, as radiologists have shown high acceptance rates of AI-based measurements. For breast screening, note the proven increase in cancer detection rates across diverse patient populations, including those with dense breasts. Integrate DeepHealth's solutions into your existing PACS/RIS for a more unified and intelligent diagnostic experience.
DeepHealth's core is its cloud-native DeepHealth OS, designed to unify data across clinical and operational workflows and personalize AI-powered workspaces. Its Diagnostic Suite can interoperate with or replace legacy PACS systems. Reporting Pro integrates with existing workflows and supports migration of templates from legacy reporting systems. DeepHealth also integrates over 140 AI algorithms from more than 75 ecosystem partners through its AI Studio Suite, orchestrating them within the clinical workflow, including worklist, viewer, and reporting modules. DeepHealth's Prostate AI solution is compatible with leading fusion biopsy systems.
Details
| Category | Clinical Decision Support & Reference, Oncology AI, Radiology & Imaging AI |
| Pricing | Contact for pricing |
| Deployment | Cloud-native or hybrid platform |
| Compliance | |
| BAA Available | Yes AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
1 AI-estimated DeepHealth has received FDA 510(k) clearance for its Breast Suite (including Saige-Dx™), Prostate AI solution within its Prostate Suite, Brain Health and Brain Age solutions within its Neuro Suite, and TechLive™ remote scanning solution. |
| Integrations | |
| EHR | Not specified |
| Specialties | Neurology, Oncology, Radiology |
What the Web Says
DeepHealth, an AI subsidiary of RadNet, focuses on developing AI tools for breast cancer detection, particularly mammography. Reviews indicate a strong potential for improving diagnostic accuracy and efficiency in radiology, though some concerns exist regarding integration and the nascent stage of AI in healthcare.
Overall: PositiveStrengths
- Improved accuracy in breast cancer detection, potentially reducing false positives and negatives.
- Increased efficiency in radiology workflows, allowing radiologists to focus on complex cases.
- FDA-cleared AI algorithms for mammography analysis.
- Backed by RadNet, a large imaging center network, providing real-world data and deployment opportunities.
- Potential to reduce radiologist burnout by automating repetitive tasks.
- Focus on a critical area of healthcare with high impact.
Limitations
- Limited independent reviews from physicians outside of RadNet's ecosystem.
- Integration challenges with existing PACS and EHR systems in diverse healthcare settings.
- The 'black box' nature of some AI models can be a concern for physician trust and accountability.
- Potential for over-reliance on AI, leading to a deskilling of radiologists.
- Cost of implementation and ongoing maintenance for smaller practices.
- Ethical considerations around AI bias and data privacy in medical imaging.
Based on reviews from: RadNet Official Website, Healthcare IT News, Radiology Business, AuntMinnie.com, PubMed (for research papers mentioning DeepHealth's technology), LinkedIn (for professional discussions)
Last updated: 2026-09-12
Ratings & Reviews
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Press & Coverage
MedTech Dive
DeepHealth gets FDA nod for AI tool that reads ultrasounds, creates reports | MedTech Dive
DeepHealth received FDA 510(k) clearance for an AI tool that interprets breast ultrasound images and generates reports, aiming to help clinicians localize and characterize suspicious lesions. This tool is expected to improve sensitivity for breast cancer detection by 8% and reduce radiologist interpretation times by 37%.
2026-08
DeepHealth (via GlobeNewswire)
DeepHealth Receives FDA Clearance for AI-Powered Breast Ultrasound
DeepHealth announced FDA 510(k) clearance for its AI-powered Breast Ultrasound solution, which automates lesion detection, characterization, and reporting to enhance standardization, accuracy, and efficiency in breast ultrasound imaging. The solution is commercially available in the US and will be implemented across RadNet's centers by the end of the year.
2026-07
OncoDaily
Inside DeepHealth: How RadNet's AI Bet Is Reshaping the Business of Radiology
RadNet's subsidiary DeepHealth has secured multiple FDA 510(k) clearances for its breast-imaging software, including tools for flagging breast arterial calcification and integrating prior exams for lesion tracking. These clearances highlight RadNet's strategic shift towards being recognized as a software business within the radiology sector.
2026-06
RadNet Inc
DeepHealth Receives Two FDA Clearances Enabling it to Add Cardiovascular Insights and Prior Exam Integration to its AI-Powered Breast Suite
DeepHealth received FDA 510(k) clearances for two new Breast Suite functionalities: Breast Arterial Calcification (BAC) Assessment and prior exam integration into ProFound Pro (now Mammo Dx). These additions aim to provide radiologists with a more complete patient overview and enhance clinical confidence by identifying potential cardiovascular disease indicators and tracking lesions over time.
2026-06
RadNet Inc
DeepHealth Launches Reporting Pro, Bringing AI Automation to Radiology Reporting
DeepHealth launched Reporting Pro, an AI-powered solution designed to automate and streamline radiology reporting by integrating speech recognition, AI-generated clinical findings, measurements, and structured reporting into one workflow. This solution aims to reduce reporting times and improve consistency, addressing the projected radiologist shortage.
2026-06
RadNet Inc
DeepHealth Unveils Next-Generation Imaging Informatics and Clinical AI Solutions at RSNA 2025, Advancing a New Standard of AI-Powered Care
DeepHealth unveiled an expanded portfolio of next-generation imaging informatics and clinical AI solutions at RSNA 2025, focusing on unifying the imaging experience and advancing population health. The new offerings span disease detection, assessment, monitoring, remote scanning, image management, and AI orchestration.
2025-11
RadNet Inc
Landmark Nature Health Study Demonstrates the Effectiveness of DeepHealth's Novel AI-Powered Breast Cancer Detection Workflow
A study published in Nature Health, the largest real-world analysis of AI-driven breast cancer screening in the U.S., demonstrated that DeepHealth's AI-powered workflow increased cancer detection rates by 21.6%. The study, which included over 579,000 women, showed consistent benefits across diverse patient populations, including those with dense breasts.
2025-11
Nature Health (via DeepHealth)
Equitable Impact of an AI-driven Breast Cancer Screening Workflow in Real-World US-Wide Deployment
This scientific publication in Nature Health in November 2025 details the AI-Supported Safeguard Review Evaluation (ASSURE) study, which found that implementing an AI workflow improved screening effectiveness for breast cancer with equitable benefits across diverse patient groups.
2025-11
Videos
Product demos, reviews, and walkthroughs for DeepHealth.
The DeepHealth Team1
RadNet
The Future of AI in Radiology with Prof. Andrea Rockall
DeepHealth
The Imaging Wire Show -- An Introduction to DeepHealth
The Imaging Wire
Advances in AI-Powered Ultrasound Diagnostics: Real-World Experiences
DeepHealth
Artificial Intelligence in Breast Cancer Detection at RadNet
RadNet
DeepHealth, the First Year, Imaging Wire Interview
DeepHealth
Frequently Asked Questions
DeepHealth refers to AI-powered solutions designed to assist in medical imaging analysis, particularly mammography. These solutions typically integrate with existing PACS (Picture Archiving and Communication Systems) to provide a 'second read' or flagging of suspicious areas, aiming to enhance diagnostic accuracy and efficiency.
DeepHealth prioritizes data security and compliance by implementing robust encryption protocols and adhering to regulations like HIPAA. Data is often de-identified before being used for AI training or analysis, and access is strictly controlled to ensure patient privacy.
DeepHealth's AI algorithms are trained on vast datasets of medical images and aim to achieve high sensitivity and specificity in detecting abnormalities. While they can identify subtle patterns and reduce false negatives, they are a decision support tool and not a replacement for human radiologists, who provide critical clinical context and judgment.
Yes, several companies offer AI solutions for medical imaging. DeepHealth often differentiates itself through its specific focus on certain modalities (e.g., mammography), its proprietary algorithms, and its integration capabilities with various PACS systems.
Pricing models for DeepHealth can vary, ranging from per-study fees to subscription-based models. Coverage by insurance or healthcare systems is evolving, with some systems beginning to recognize the value of AI in improving diagnostic outcomes and efficiency.
DeepHealth typically provides comprehensive training for physicians and their staff on how to effectively use and integrate their AI solutions into daily practice. This often includes on-site training, online modules, and ongoing technical support to ensure smooth implementation and utilization.
DeepHealth's algorithms are continuously refined to minimize false positives and false negatives. While no AI is perfect, the goal is to reduce radiologist workload by highlighting areas of concern, allowing for earlier detection in some cases and reducing unnecessary callbacks in others, ultimately aiming to improve patient outcomes.
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| Last Verified: 2026-06-11 | First Added: 2026-06-11
Investors who backed DeepHealth
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