Atlas by Gradient Health
Overview
Atlas by Gradient Health is a medical data platform designed for artificial intelligence (AI) developers. It provides access to de-identified medical imaging, EHR, and multimodal datasets for training, testing, and validating AI models. The platform is intended for medical AI developers, data science teams, health technology companies, and life sciences organizations.
- What it does: Atlas enables users to search and access de-identified patient-level datasets, including medical imaging (such as CT, MRI, X-ray, ultrasound, mammography), EHR data, pathology, laboratory, and ECG data. It supports the development of AI models that can account for disease progression, patient history, and real-world clinical context.
- Who it is for: The platform primarily serves medical AI developers and researchers who require diverse and high-quality medical data to build and validate AI technologies.
- How it fits a clinical or practice workflow: Atlas is not a direct clinical tool for patient care. Instead, it supports the upstream development of AI tools that may eventually integrate into clinical workflows. It streamlines the process of sourcing and preparing data for AI model development, which can accelerate the creation of AI applications for various medical specialties.
- Notable capabilities: Atlas offers a natural-language AI Search Assistant to help developers find and refine datasets more quickly. It provides access to a large library of de-identified medical data, with capabilities for filtering by data type, modality, disease, keywords, and demographics. The platform also supports collaboration among development teams.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- Access to large, diverse, de-identified medical datasets (20M+ studies, 300M+ patient studies)
- Multimodal data (EHR, pathology, radiology, imaging, clinical records)
- Advanced search capabilities with hundreds of DICOM tags, series-level metadata, and longitudinal patient-level search
- AI Search Assistant for natural-language dataset discovery
- Rapid data delivery (as little as 48 hours)
- Tools for assessing dataset diversity and comprehensiveness
- Machine learning ready formats
- Collaboration tools for shared cohorts and workspaces
- De-identification with stringent privacy and security controls
- Support for regulatory evidence generation
Use Cases
- Training AI models
- Testing and validating AI models
- Model evaluation
- Foundation model development
- Regulatory evidence generation
- Reducing bias in medical AI
What Physicians Need to Know
For physicians and healthcare providers, Atlas by Gradient Health offers a secure and compliant platform to contribute de-identified medical data, generating a new revenue stream for their institutions while fueling the development of advanced medical AI. The platform's robust de-identification processes and HIPAA compliance ensure patient privacy is protected. By partnering with Gradient Health, healthcare organizations can contribute to more diverse and representative datasets, which is crucial for building unbiased and effective AI models that can improve patient outcomes and address health inequalities. The platform also aims to reduce the operational and technical burden on data partners, making data sharing more straightforward.
Atlas by Gradient Health is available on Google Cloud Marketplace, enabling AI developers and research institutions to securely access diverse, de-identified medical imaging data directly within Google Cloud. Gradient Health partners with companies like DataFirst, allowing imaging providers using DataFirst's Silverbacku00ae Workflow Engine to seamlessly enable secure, de-identified data sharing for AI development. The SilverBacku00ae engine is an interoperable enterprise imaging platform with an integrated HL7 engine. The platform supports hundreds of DICOM tags and is expanding to include EHR, pathology, labs, and ECG data, facilitating multimodal AI development.
Details
| Category | Developer Tools & APIs, Pathology AI, Radiology & Imaging AI |
| Pricing | Contact for pricing — 7-day free trial available |
| Deployment | Cloud-based (available on Google Cloud Marketplace) |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
Unknown AI-estimated Atlas can support teams building regulatory evidence by helping them access de-identified datasets selected for relevance, representativeness, and intended use. The specific regulatory suitability of a dataset depends on the model, claim, market, and validation plan. |
| Integrations | |
| EHR | Not specified |
| Specialties | Pathology, Radiology |
What the Web Says
Atlas by Gradient Health is a platform designed to facilitate the secure sharing and annotation of medical imaging data for AI development. Reviews highlight its focus on data privacy, ease of use for researchers, and its potential to accelerate medical AI innovation by providing access to diverse datasets. However, some feedback suggests a need for broader adoption and more extensive real-world physician testimonials.
Overall: PositiveStrengths
- Secure and compliant data sharing for medical imaging (HIPAA, GDPR)
- Facilitates access to diverse, high-quality medical datasets for AI training
- Streamlines data annotation workflows
- Focus on data privacy and de-identification
- Potential to accelerate medical AI development and research
- User-friendly interface for researchers and data scientists
Limitations
- Limited public physician reviews available
- Newer platform, so long-term impact and widespread adoption are still developing
- Specific pricing details are not readily public, requiring direct contact
- Reliance on data contributors for dataset diversity and volume
- Potential learning curve for new users unfamiliar with the platform's specific tools
- Integration challenges with existing hospital IT infrastructure not explicitly detailed in public reviews
Based on reviews from: Gradient Health official website, TechCrunch articles, AI in Healthcare news outlets, LinkedIn discussions (general sentiment), Industry analyst reports (limited public access), Academic papers citing Gradient Health (indirect reviews)
Last updated: 2026-09-03
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