HOPPR Demonstrates Agentic AI Orchestration at RSNA 2025's 'Radiology Reimagined'

Live demonstration showcases how Model Context Protocol enables AI agents to autonomously coordinate imaging workflow and opportunistic screening 

HOPPR's agentic AI workflow orchestration is being featured as part of the RSNA future technology exhibit to demonstrate ongoing research and innovation. This technology is currently in development and is not commercially available.

HOPPR's agentic AI workflow orchestration is being featured as part of the RSNA future technology exhibit to demonstrate ongoing research and innovation. This technology is currently in development and is not commercially available. 

• HOPPR's agentic AI platform autonomously queries EMRs, retrieves priors, and integrates lab data to provide comprehensive clinical context 

• Model Context Protocol enables intelligent coordination between AI models, PACS, EMR systems, and reporting platforms 

• Real-world case shows how a single ED scan identifies 8+ conditions for preventive care through opportunistic screening 

• Built on modern standards: DICOMweb, FHIR, and MCP 

Chicago, IL - December 1, 2025 – HOPPR, a company focused on transforming how AI applications are developed for medical imaging, announced today that it will showcase agentic AI orchestration in the "Radiology Reimagined: AI, innovation and interoperability in practice" demonstration at the 111th Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA 2025), Nov. 30 – Dec. 3 at McCormick Place in Chicago. 

The demonstration previews an emerging paradigm in radiology AI that moves beyond passive algorithms that simply flag findings. HOPPR's agentic AI concept envisions an intelligent system capable of autonomously retrieving relevant priors, querying lab values, and integrating clinical context from electronic medical records. Built on Model Context Protocol (MCP), it illustrates the potential for coordinating real-time communication between AI models, PACS, EMR systems, and reporting platforms to deliver comprehensive, context-aware insights that could redefine how radiology workflows operate in the future. 

Turning Every Scan into a Preventive Health Opportunity 

The demonstration presents a theoretical case study following "Frank," a 64-year-old presenting to the emergency department with flank pain. In this illustrative scenario, while the ED physician orders a CT to evaluate suspected kidney stones, HOPPR's agentic AI concept identifies a range of incidental findings that could warrant follow-up, such as coronary and vascular calcifications, a hepatic lesion, hepatic steatosis, osteopenia, sarcopenia, and degenerative spine changes. 

"This example demonstrates how agentic AI can transform opportunistic screening," said Khan Siddiqui, MD, CEO and Co-Founder of HOPPR. "By autonomously retrieving relevant labs, comparing to prior imaging, and integrating clinical context, HOPPR turns a single ED visit into a comprehensive preventive health assessment. This represents the future of healthcare AI with the potential to deliver meaningful impact in real-world clinical practice." 

Model Context Protocol: The Engine of Agentic AI 

The Model Context Protocol (MCP) represents a forward-looking approach to how future AI systems could operate, enabling AI agents to actively fetch the information they need rather than waiting for data to be pushed. In the demonstration, attendees will explore the potential of MCP in action: 

• Retrieving prior imaging studies to assess interval changes in kidney stones and other findings 

• Querying the EMR for lab values relevant to detected findings (metabolic panels for renal stones, lipids for vascular calcifications) 

• Pulling clinical history to contextualize recommendations 

• Coordinating findings across multiple AI models running simultaneously 

This intelligent orchestration happens automatically in the background, with all results flowing seamlessly into the radiologist's workflow. 

Built on Modern Healthcare Standards 

HOPPR's agentic AI vision is designed around three foundational technologies: 

• DICOMweb for efficient, standards-based image exchange 

• FHIR (Fast Healthcare Interoperability Resources) for clinical data integration 

• Model Context Protocol for AI coordination and intelligent data retrieval 

This modern interoperability stack illustrates how next-generation AI systems could operate within existing healthcare IT environments while adding an intelligent layer needed for true agentic behavior. 

Real-World Integration 

This year's Radiology Reimagined explores how emerging AI technologies could transform the diagnostic radiology workflow, featuring 18 products from 16 vendors that point toward a more connected and intelligent future. Through realistic clinical scenarios, the collaborative exhibit highlights the direction of AI integration in radiology by demonstrating the potential for seamless, cross-system interoperability and workflow-aware intelligence. 

"Healthcare AI will reach its full potential when it's designed for the realities of clinical practice," said Dr. Siddiqui. "At RSNA 2025, we're showcasing forward-looking technology that is paving the way for developers to build and deploy real-world imaging solutions faster, with the workflow intelligence needed for true clinical adoption." 

Attendees are invited to visit booth 5104 throughout the conference. On Wednesday, Dec. 3, the AI Showcase Theater session "Behind the Scenes of Radiology Reimagined" provides an inside look at how the demonstration is designed and executed. 

Radiology Reimagined runs Sunday, Nov. 30, through Wednesday, Dec. 3, 10 a.m. to 5 p.m. CT at McCormick Place, South Hall A, Level 3 – AI Showcase, Booth 5104. 

For more information or to register, visit RSNA.org/annual-meeting. 

Find out more about HOPPR online here: www.hoppr.ai. 

About HOPPR 

HOPPR is a health technology company created by radiologists and technologists with a passion for democratizing AI development in medical imaging. Its flagship product, the HOPPR™ AI Foundry, unites trusted data, foundation models, and strong quality controls to streamline and scale responsible AI development. It is built under a Quality Management System (QMS) that ensures traceability, compliance, and consistency across the lifecycle. The Foundry also features patent-pending privacy and security safeguards to protect data and promote responsible innovation from concept to deployment.