HOPPR, a Chicago-based company focused on medical imaging AI development, will launch the HOPPR™ AI Foundry at the Radiological Society of North America (RSNA) 2025 Annual Meeting. The platform addresses a persistent challenge in medical imaging AI: developers have historically faced a choice between innovation speed and regulatory compliance. The Foundry combines proprietary foundation models, curated datasets with established provenance, and an integrated Quality Management System designed to support both rapid development and regulatory preparation.
The platform will debut as healthcare organizations increasingly seek validated AI tools beyond proof-of-concept pilots. HOPPR positions the Foundry as the first purpose-built AI development platform for health imaging accessible to developers that operates under a Quality Management System aligned with ISO 13485, IEC 62304, ISO/IEC 42001, and ISO 14971 standards.
Foundation Models Meet Healthcare’s Compliance Framework
The HOPPR™ AI Foundry integrates vision transformer-based foundation models built using self-supervised learning, enabling developers to adapt models across classification and anomaly detection tasks through intuitive workflows. The platform offers access to one of the largest, curated medical imaging datasets in the private industry, comprising over 19 million studies from 18 partner imaging sites, according to the company.
“The HOPPR™ AI Foundry represents a breakthrough in innovation infrastructure for trustworthy, scalable AI in medical imaging. Developers no longer need to choose between innovation or compliance.” — Khan Siddiqui, MD, CEO and Co-Founder, HOPPR
“The HOPPR™ AI Foundry represents a breakthrough in innovation infrastructure for trustworthy, scalable AI in medical imaging,” said Khan Siddiqui, MD, CEO and Co-Founder of HOPPR. “Developers no longer need to choose between innovation or compliance. The HOPPR AI Foundry is built to accelerate progress, from experimentation to real-world readiness, while maintaining the traceability, documentation, and quality controls required to enable regulatory compliance.”
The platform’s integrated QMS provides version control, traceability, and documentation to support lifecycle management—capabilities that typically require separate infrastructure investments. Developer-friendly APIs allow teams to train, evaluate, and embed AI models directly into imaging workflows or partner applications.
Data Provenance as Development Infrastructure
Data quality and provenance remain persistent challenges in the development of medical imaging AI. The Foundry addresses this through curated datasets with known origins, allowing developers to either use HOPPR’s labeled and validated datasets or bring their own data for model fine-tuning. This flexibility targets different development scenarios, from early-stage experimentation to production-ready validation.
The platform’s architecture separates it from AI orchestration platforms like deepcOS and marketplace ecosystems such as CARPL.ai, which focus on deploying and monitoring existing AI models in clinical workflows. HOPPR instead provides the upstream development environment where models are built and refined before deployment—filling a distinct infrastructure gap in the medical imaging AI ecosystem.
Strategic Context: The AI Development Infrastructure Market
The medical imaging AI market has evolved from narrow point solutions addressing specific clinical tasks to more comprehensive platforms. While companies like Aidoc and DeepHealth focus on clinical AI applications and deployment infrastructure, and technology giants including NVIDIA and Google Cloud offer broad AI development tools applicable to healthcare, HOPPR’s Foundry occupies a more specialized position: a development platform purpose-built exclusively for medical imaging that embeds regulatory compliance from the start.
Project MONAI, the open-source framework founded by NVIDIA and King’s College London, provides domain-optimized tools for medical imaging AI development. HOPPR differentiates by offering a managed platform with integrated compliance infrastructure and proprietary foundation models alongside the tooling, targeting organizations that require both development acceleration and regulatory preparation support.
“We can leverage our data and clinical expertise to fine-tune models, rapidly test in production, and then scale as we see improved outcomes. Foundry is instrumental in the holy grail of adaptive learning in clinical workflow.” — Sham Sokka, Chief Operating and Technology Officer, DeepHealth
“For us, the HOPPR AI Foundry is transformative,” said Sham Sokka, Chief Operating and Technology Officer at DeepHealth. “We can leverage our data and clinical expertise to fine-tune models, rapidly test in production, and then scale as we see improved outcomes. Foundry is instrumental in the holy grail of adaptive learning in clinical workflow.”
Clinical Validation and Market Access Challenges
The Foundry’s emphasis on integrated quality management systems addresses a documented gap in medical imaging AI adoption. According to research presented at industry conferences, the medical imaging AI market remains fragmented with numerous unproven solutions, and many certified radiology AI tools are approved but not independently validated for clinical outcomes, workflow integration, and governance requirements.
The platform’s approach to adaptive learning—enabling models to evolve based on real-world performance while maintaining traceability—targets what DeepHealth’s Sokka described as the “holy grail” of clinical AI deployment. This capability becomes increasingly relevant as healthcare organizations move beyond pilot programs to production-scale implementations requiring ongoing model refinement.
Developer Access and Implementation
HOPPR is exhibiting the Foundry at RSNA in South Hall Booth #4000, with demonstrations of the platform’s fine-tuning workflows and evaluation tools. The company’s mission centers on democratizing access to responsible AI development in medical imaging by removing infrastructure, data management, and compliance barriers that typically require significant capital and expertise investments.
The platform’s architecture reflects lessons from early medical imaging AI deployments, where developers often discovered regulatory and quality management requirements late in development cycles. By embedding these frameworks from the beginning, HOPPR aims to compress the timeline from concept to regulatory submission—a critical factor for smaller organizations and academic developers without dedicated regulatory infrastructure.
The medical imaging AI landscape continues to evolve rapidly, with platforms like Microsoft Azure AI Foundry introducing multimodal healthcare models and orchestration capabilities. HOPPR’s focused approach on medical imaging development infrastructure with integrated compliance represents a bet that specialized tools purpose-built for healthcare’s regulatory environment will enable faster, safer AI adoption than general-purpose platforms adapted for medical use.
For healthcare organizations evaluating AI development strategies, the Foundry offers an alternative to building internal infrastructure or adapting general-purpose tools—provided the proprietary platform approach aligns with their development workflows and strategic priorities.
— This original article was created with AI support.
