Atlas, built for medical AI developers.

Atlas is Gradient Health’s medical data platform for AI developers. It helps teams discover and access deidentified medical imaging, EHR, and multimodal datasets for training, testing, validation, and regulatory evidence generation.

Atlas is built for medical AI developers, data science teams, foundation model teams, healthtech companies, and life sciences organizations that need high-quality medical data to develop, validate, or improve AI models.

Atlas provides access to deidentified medical imaging data, including radiology exams, as well as multimodal medical data such as pathology reports, ophthalmology reports, and EHR-linked datasets as well as other clinical records where available.

Yes. Atlas can support multimodal medical AI development by helping teams access datasets that combine imaging with additional clinical context, such as EHR data, diagnoses, reports, claims, or other structured and unstructured data sources.

Yes. Atlas enables developers to access deidentified patient-level datasets that include multiple exams, records, reports, or clinical events over time. This helps AI teams build models that account for disease progression, patient history, treatment pathways, follow-up imaging, and real-world clinical context, rather than relying only on single images or isolated records.

Yes. Atlas can help foundation model teams access large-scale, diverse, deidentified medical datasets for model pretraining, fine-tuning, evaluation, and validation.

Atlas helps AI developers identify and build relevant datasets based on clinical, imaging, demographic, and multimodal criteria. This can reduce the time and complexity involved in finding usable medical data for AI development.

Yes. Data accessed through Atlas is deidentified before it is made available to AI developers. Gradient Health applies stringent privacy and security controls to protect patient information and support responsible data use.

Atlas data comes from Gradient Health’s global network of healthcare data partners who provide deidentified medical imaging and clinical data for responsible AI development.

AI developers can use Atlas datasets for training, testing, validation, model evaluation, foundation model development, and regulatory evidence generation.

Atlas helps reduce bias by enabling access to broader, more diverse datasets across different populations, geographies, imaging devices, clinical settings, and disease presentations.

Yes. Atlas can support access to datasets that combine medical imaging with EHR data or other clinical context, depending on the project requirements and available data.

Atlas can support access to medical imaging datasets across modalities such as CT, MRI, X-ray, ultrasound, mammography, DEXA, OCT, fundus imaging, and other imaging types where available.

Timelines depend on the dataset requirements, project scope, and data availability, but can be as little as 48 hours. Atlas is designed to make medical data discovery and access faster, more structured, and more repeatable.

Public datasets are often limited in size, diversity, metadata, and commercial usability. Atlas is designed to help AI teams access larger, diverse, deidentified medical datasets that can better support real-world development, validation, and regulatory needs.

Atlas can support teams building regulatory evidence by helping them access deidentified datasets that are selected for relevance, representativeness, and intended use. The specific regulatory suitability of a dataset depends on the model, claim, market, and validation plan.

AI developers and commercial teams can request an Atlas trial through the Atlas page.