HOPPR and CARPL.ai partner to give customers access to foundation models for building and fine-tuning radiology AI. Oct 07, 2026, 09:01 ET CUPERTINO, Calif., Oct. 7, 2026 /PRNewswire/ - CARPL.ai, the world's largest radiology AI marketplace and enterprise imaging AI platform, today announced a partnership with HOPPR, a company transforming how AI is developed for medical imaging. The collaboration gives healthcare providers access to HOPPR's foundation models to securely develop, fine-tune, and deploy custom radiology AI applications through the CARPL platform. Building these tailored models has traditionally required massive computational resources, large proprietary datasets, and significant technical overhead. This partnership removes those barriers. Radiology teams can now leverage HOPPR's advanced vision and language foundation models as a starting point. Using their own local imaging data or curated reference datasets, they can securely fine-tune applications such as automated report generation, ensuring outputs align with institutional terminology, reporting styles, and clinical requirements. The result is a seamless path from model training to deployment. These applications integrate directly into the CARPL platform, where healthcare providers can validate, deploy, and monitor them within their existing PACS infrastructure, without disrupting workflows. "As we help some of the world's largest radiology groups and academic medical centers adopt AI, a new need emerged - a need to build and fine-tune their own AI models, using their own data based on their own use case. This partnership will now fast-track that," said Vidur Mahajan, CEO at CARPL.ai. "HOPPR is quickly becoming the undisputed leader in the foundational AI space for radiology, and we are thrilled to offer them through our platform." "Accessing and curating millions of imaging studies to build AI models is time-consuming, expensive and difficult to scale. HOPPR's foundation models give radiologists and AI developers a starting point to build their own models using a fraction of the data they would otherwise need," said Dr. Khan Siddiqui, Co-Founder, Chairman & CEO at HOPPR. "Our partnership with CARPL connects that development process to clinical deployment, giving healthcare providers a single platform to procure, integrate, and use AI models built on HOPPR's platform." Looking ahead, CARPL.ai and HOPPR plan to expand capabilities across a wider variety of imaging modalities. The companies will also leverage CARPL's post-market surveillance tools to continuously monitor model performance, ensuring they remain accurate and consistent over time. About HOPPR Founded in 2019, HOPPR brings together experts in clinical radiology, AI development, and healthcare commercialization to advance the development of transparent and scalable AI for medical imaging. The HOPPR(R) AI Foundry is a secure development platform designed for building, fine-tuning, validating, and hosting AI models for medical imaging. The platform provides curated datasets, traceable development workflows, and secure infrastructure that support responsible AI development aligned with industry quality and regulatory standards. HOPPR(TM) AI Foundry holds SOC 2 Type II attestation and HITRUST e1 Certification and is HIPAA-compliant. HOPPR also offers Forward Deployed Services, embedding HOPPR's machine learning scientists, software engineers, data scientists, and clinical experts directly within your team to accelerate development at every stage. For more information, visit www.hoppr.ai. About CARPL.ai CARPL.ai is an end-to-end enterprise imaging AI platform, and the world's largest radiology AI marketplace offering 300+ applications from 100+ AI Partners. With regulatory clearances across multiple regions including the FDA, CE, ANVISA, TGA, and HSA, CARPL.ai enables radiologists and researchers to easily access, assess, and integrate radiology AI applications into their clinical practice. CARPL.ai provides a single user interface, a single data channel, and a single procurement channel for the testing, deployment, and monitoring of AI applications in clinical radiology workflows. The CARPL.ai platform is used by four out of the world's top five radiology groups, and integrates with leading PACS systems, including Philips, AGFA HealthCare, DeepHealth, Intelerad, RamSoft, and Paxera Health, ensuring seamless interoperability across imaging environments. Learn more at www.carpl.ai SOURCE HOPPR

HOPPR unveils new AI foundation model for Chest CT. The HOPPR EF Chest CT Narrative Model reportedly provides narrative language describing pertinent findings across pulmonary, upper abdominal, cardiac, mediastinal and soft tissue regions with processing of 3D chest CT volumes. Adding a fifth foundation model to its AI Foundry portfolio, HOPPR has introduced the HOPPR EF Chest CT Narrative Model. Trained on a large proprietary chest CT dataset drawn from multiple facilities, the offers narrative descriptions of chest CT findings across pulmonary, upper abdominal, cardiac, mediastinal and soft tissue regions with processing of 3D chest CT volumes. HOPPR added that the AI foundation model also includes coverage of conditions such as aortic injury, pneumothorax and pulmonary embolism. The company noted that theHOPPR EF Chest CT Narrative Model is available through HOPPR Forward Deployed Services that provide expertise in clinical workflow, machine learning and deployment support to facilitate development and customization of the foundation model to the needs of the radiology department or facility. "Chest CT is one of the most information-dense studies in radiology. Getting AI to work well across everything it captures - the lungs, the heart, the aorta - is a genuinely hard problem, and we are pleased with what this model can do," noted Khan Siddiqui, MD, the co-founder and CEO of HOPPR. "But the model is only part of what we are building. Our AI Foundry and Forward Deployed Services include secure infrastructure, curated data, and the clinical and technical expertise to help any organization move from a foundation model to a working application, regardless of where they are starting from. That is what makes this more than a model release."