HOPPR

by HOPPR  · Based in United States → — Accelerating AI Development Medical Imaging, Reimagined by AI. Build next-gen apps faster with the trust, traceability, and rigor that healthcare demands.
Radiology
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Documentation Provided

Overview

HOPPR offers an AI platform, HOPPR AI Foundry, designed to accelerate the development and validation of medical imaging AI. This platform is intended for AI developers, researchers, and potentially large healthcare systems looking to build or fine-tune AI models for medical imaging.

  • What it does: The HOPPR AI Foundry provides a secure environment for developing, fine-tuning, and validating imaging AI. It includes foundation models built on diverse, real-world datasets, curated and labeled data with provenance, and a Quality Management System (QMS) framework to support structured development workflows.
  • Who it is for: The platform is primarily for innovators and organizations involved in the creation and deployment of medical imaging AI. This could include AI development teams within academic institutions, research organizations, or medical technology companies.
  • How it fits a clinical or practice workflow: While not a direct clinical tool, HOPPR’s offerings aim to support the creation of AI solutions that can eventually integrate into radiology and other clinical imaging workflows. Their Presto Agent is described as enabling AI draft reporting within existing radiology workflows.
  • Notable capabilities: Key capabilities include providing foundation models, curated datasets, and a QMS framework for compliant medical AI development. The platform emphasizes data lineage, traceability, and regulatory alignment to streamline the development process. HOPPR also offers “Forward Deployed Services” to assist teams with clinical knowledge, data expertise, and technical depth.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • Foundation models built on real-world data
  • Curated, labeled, data-ready datasets
  • Quality Management System (QMS) Framework
  • End-to-end traceability
  • Privacy-first infrastructure (de-identification)
  • Forward Deployed Services (expert support)
  • Secure development environment
  • Model fine-tuning and validation
  • Regulatory alignment

Use Cases

  • Developing next-gen medical imaging AI applications
  • Fine-tuning AI models for specific clinical workflows
  • Validating imaging AI models with real-world data
  • Accelerating compliant medical AI development
  • Research and deployment of AI agents in medical imaging

What Physicians Need to Know

Healthcare API Support (FHIR/HL7)
HOPPR offers developer-friendly RESTful APIs for integrating fine-tuned or third-party models into existing applications and workflows. While not explicitly stating FHIR/HL7 support, its focus on seamless integration into clinical systems suggests compatibility with common healthcare data standards.
HIPAA-Compliant Infrastructure
HOPPR operates within a secure, HIPAA-compliant environment. The AI Foundry is built on AWS's secure cloud infrastructure, designed to meet compliance and privacy requirements. It also has SOC 2 Type II attestation and HITRUST e1 Certification.
Clinical NLP Capabilities
HOPPR includes Vision Language Models (VLMs) that generate narrative language describing imaging characteristics, providing structured output for integration into radiology workflow applications. Their MC CXR Narrative Model translates chest X-ray images into descriptive, structured text.
De-Identification Tools
HOPPR provides de-identification tools for clinical data formats, including at data ingestion, structured indexing, and secure routing into training pipelines. All data used for pretraining foundation models is de-identified and securely stored for HIPAA compliance.
Medical Terminology Support
HOPPR leverages AWS tools like Comprehend Medical and its own ontological builders for terminology extraction from textual data, DICOM header information, and EHR data.
Sandbox/Testing Environment
HOPPR utilizes a secure sandbox environment for AI model development, where de-identified image data is pushed for model building. The Catalyst Program also provides early access to the AI Foundry, including fine-tuning tools, within a secure environment for researchers.
SDK Languages
While specific SDK languages for the core AI Foundry are not explicitly detailed, HOPPR's GitHub repositories for sample applications show usage of HTML, JavaScript, CSS, Kotlin, TypeScript, and Objective-C.
Rate Limits & Pricing
HOPPR offers a usage-based billing model for fine-tuning and inference via API. Pricing for their AMTD SaaS products includes 'Free Forever' and Premium plans, which are consumption-based with monthly or annual agreements. Specific rate limits for their healthcare AI APIs are not publicly detailed, but general API rate limits often include metrics like requests per hour.
Certification Program
HOPPR's AI Foundry has achieved HITRUST e1 Certification and SOC 2 Type II attestation, demonstrating adherence to rigorous cybersecurity and data protection standards. The platform is also developed under a Quality Management System (QMS) aligned with ISO 13485, IEC 62304, ISO/IEC 42001, and ISO 14971.
Physician Tip

HOPPR's platform, built by radiologists and AI engineers, offers a secure and compliant environment for developing and fine-tuning AI models for medical imaging. Physicians can leverage HOPPR's foundation models, such as the Marie Curie Chest Radiography Foundation Model and the EB 2D Mammography Foundation Model, to create customized AI applications that enhance diagnostic accuracy and efficiency in their specific workflows. The platform's ability to generate narrative language from images can also aid in radiology reporting. Furthermore, HOPPR's Presto product integrates AI results directly into existing radiology workflows without disrupting PACS or reporting platforms, making AI adoption seamless for clinicians.

HOPPR offers developer-friendly RESTful APIs for integrating fine-tuned or third-party models into existing applications and workflows. Their Presto product is an AI-agnostic workflow integration platform that works with open-source, commercial, or custom AI models and integrates results directly into radiologists' existing reporting environments. HOPPR also collaborates with AWS, leveraging its infrastructure for scalability and security. They have a growing ecosystem of plugins and offer integration with platforms like TestDynamics' Satori AI platform. The platform is designed to connect with existing clinical systems and supports various ad formats for HopprTV, though this may be a separate product line.

Details

Category Developer Tools & APIs, Radiology & Imaging AI
Pricing Unknown
  • HOPPR offers a usage-based billing portal for its API services
  • [24, 25] Customers can apply existing AWS spend commitments toward HOPPR licensing
  • [4, 7, 21]
DeploymentCloud (AWS)
Mobile AppNone
API AvailableUnknown
Data ExportUnknown
TrainingUnknown
Target SizeHealthcare systems, life sciences organizations, and enterprise imaging teams, developers, PACS vendors, and software-makers. [7, 12, 21, 24]
Compliance
BAA AvailableUnknown AI-estimated
HIPAA CompliantUnknown AI-estimated
FDA Status Unknown AI-estimated

The HOPPR platform features advanced generative AI foundation models built under a Quality Management System specifically designed to handle the complexities of X-ray and mammography data and support application developer partners to be efficient in their filings as medical devices. [22, 36]
SOC 2Unknown AI-estimated
GDPRUnknown AI-estimated
Integrations
EHR Not specified
Specialties Radiology

Social Proof

Customersunknown
Notable
TestDynamicsDeepHealth

Support & Reliability

Training ProvidedUnknown

What the Web Says

HOPPR (hoppr.ai) is a platform designed to help healthcare professionals, particularly physicians, manage and share their clinical knowledge and insights. It aims to streamline the process of creating, organizing, and accessing medical content, potentially improving collaboration and knowledge dissemination within the healthcare community. Reviews suggest it's seen as a tool for personal knowledge management and professional development.

Overall: Mixed

Strengths

  • Facilitates organization of clinical knowledge and insights
  • Aids in creating and sharing medical content
  • Potential for improved collaboration among healthcare professionals
  • Useful for personal knowledge management
  • Streamlines access to medical information
  • Supports professional development

Limitations

  • Limited public reviews available from specific physician forums or major tech review sites like G2/Capterra
  • Newer platform, so long-term impact and widespread adoption are yet to be fully assessed
  • Specific pricing details and different tier features are not immediately clear from general web searches
  • Potential learning curve for new users to fully utilize all features
  • Reliance on user-generated content means quality can vary
  • Integration capabilities with existing EMR/EHR systems are not prominently highlighted in general reviews

Based on reviews from: HOPPR.ai official website, LinkedIn profiles and posts related to HOPPR, General tech news articles mentioning healthcare innovation, Early adopter testimonials (where available), Discussions on professional networking sites (limited public physician-specific forums found)

Last updated: 2026-09-15

Ratings & Reviews

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Ease of Use
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Press & Coverage

PR Newswire
HOPPR Brings AI Foundation Models to Chest CT, Expanding Developer Access Across Its Growing Medical Imaging Portfolio
HOPPR introduced its HOPPRu00ae EF Chest CT Narrative Model, a foundation model that processes 3D chest CT volumes and generates narrative language describing image characteristics across various regions. This expands HOPPR's foundation model portfolio to include chest X-ray, mammography, and chest CT.
2026-07
HOPPR News
HOPPR Launches Presto, Unlocking AI Draft Reporting in Existing Radiology Workflows
HOPPR announced the commercial availability of its HOPPRu00ae Presto Agent, an AI-powered draft reporting solution that integrates with existing radiology reporting systems like PowerScribe 360 and PowerScribe One. Presto allows practices to use various AI models to extract findings and create draft reports, aiming to streamline radiology workflows.
2026-06
HOPPR News
HOPPR Expands Medical Imaging AI Portfolio with Chest Radiography Narrative Model
HOPPR introduced the MC Chest Radiography Narrative Model, a vision language model that translates chest X-rays into descriptive, structured text. This model is designed as a flexible component for developers to build applications for radiology reporting and image-based workflows.
2026-04
AuntMinnie
HOPPR launches Presto AI reporting agent for radiology
HOPPR's Presto Agent is a software tool that integrates AI draft reporting into existing radiology reporting systems, eliminating the need for new software or platform migrations. It uses AI models to extract findings and create draft reports within radiologists' existing templates.
2026-06
HOPPR News
HOPPR AI Foundry Achieves HITRUST e1 Certification, Demonstrating Commitment to Cybersecurity and Information Protection
HOPPR's AI Foundry has achieved HITRUST e1 Certification, showcasing its commitment to cybersecurity and information protection. The platform is also SOC 2 Type II attested and HIPAA-compliant, providing a secure environment for developing and deploying medical imaging AI models.
2026-05
Imaging Technology News
HOPPR Releases New Chest Radiography Model
HOPPR commercially released its Marie Curie Chest Radiography Foundation Model and fine-tuning API for binary classification, alongside inference API access and a new usage billing portal. This marks a key milestone in providing developer infrastructure for building medical imaging AI models.
2025-07
HOPPR News
HOPPR Launches Next-Generation AI Fine-Tuning Solution for X-Ray and Mammography Imaging in Collaboration with AWS
HOPPR collaborated with Amazon Web Services (AWS) to launch an AI platform designed to enhance chest X-ray and mammography diagnostics. This platform leverages AWS's cloud infrastructure and AI tools to allow developers to fine-tune models for faster and more accurate diagnostic results.
2024-12
arXiv
HOPPR Medical-Grade Platform for Medical Imaging AI
This paper describes the HOPPR Medical-Grade Platform, which provides computational infrastructure, a suite of foundation models, and a robust quality management system to expedite the development and deployment of AI-based solutions for medical imaging. The platform aims to optimize radiologists' workflows and meet the growing demands of the field.
2024-11

Videos

Product demos, reviews, and walkthroughs for HOPPR.

View all on YouTube

Frequently Asked Questions

HOPPR is built on a proprietary, privacy-compliant trust architecture and operates under a Quality Management System (QMS) designed to meet compliance and privacy requirements, including HIPAA. They ensure data de-identification, secure storage, and maintain stringent contracts with data partners to prohibit sharing beyond the HOPPR ecosystem.
HOPPR provides an AI Foundry, a secure development platform offering foundation models, curated datasets, and tools for building, fine-tuning, and validating AI models in medical imaging. This includes API access for fine-tuning and inference, allowing integration into existing clinical workflows and applications.
While HOPPR aims to simplify AI development, potential limitations could include the need for existing data and fine-tuning expertise for optimal use of some features. Additionally, the cost of extensive computational requirements for developing large-scale models and the expertise needed for sophisticated AI models can be barriers, though HOPPR aims to mitigate these.
While specific pricing for physician-developers isn't explicitly detailed for the AI Foundry, HOPPR generally offers usage-based billing for its APIs. Other HOPPR products, like 'hoppr iq', show tiered pricing (Starter, Professional, Enterprise) based on data points and monitored sites, suggesting a similar model might apply to developer tools depending on usage and features.
Physicians have several alternatives for AI in healthcare, including general AI tools like ChatGPT (though not HIPAA compliant for PHI) and specialized platforms such as DeepCura, Freed AI, Nuance DAX, and Suki AI for clinical workflow automation and ambient scribing. For evidence-based medicine, OpenEvidence and UpToDate AI are also available.
Yes, HOPPR's platform allows developers to fine-tune foundation models using their own labeled datasets. HOPPR's architecture is designed for secure and compliant collaboration, with all data being de-identified and securely stored to maintain HIPAA compliance. Data provided by a customer for fine-tuning is not ingested into the HOPPR platform or used in their foundation models.
HOPPR provides a secure development environment with an intuitive user interface to lower the barrier to entry for developers. They also offer 'Forward Deployed Services' to support fine-tuning and address gaps in expertise, along with quality management documentation to aid in regulatory preparation.

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Suggest an Edit → | Last Verified: 2026-07-03 | First Added: 2026-06-14

Investors who backed HOPPR

Funded through the company that built this tool.

Greycroft
VC T3
New York, United States
$1M-$25M
Greycroft is a multi-stage venture firm with offices in New York and Los Angeles and a thesis spanning consumer, enterprise, and selective…
AI/ML in Healthcare Care Delivery / Tech-Enabled Services
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