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Gus Iversen, Editor in Chief | January 29, 2026
Radiology Partners has entered a strategic collaboration with the AI Development and Evaluation Lab at Stanford Radiology to study the safety and performance of artificial intelligence tools in medical imaging.
The partnership will combine Radiology Partners' large-scale clinical AI deployments, delivered through its Mosaic Clinical Technologies division, with the AIDE Lab’s academic research into AI safety and equity. Both organizations aim to codevelop evaluation frameworks and monitoring strategies that can be adopted across health systems.
Dr. Nina Kottler, chief medical AI officer at Mosaic, said the collaboration will help build tools and standards that function reliably in clinical environments. “By combining their academic expertise with RP’s scale and clinical integration experience, we have the opportunity to develop and share evidence-based systems for AI validation and monitoring that work in real-world practice and ultimately benefit patients everywhere,” she said.
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The California-based AIDE Lab and Nashville, Tennessee-based Radiology Partners have already begun joint research, translating Mosaic’s experience with AI in practice into reproducible studies and peer-reviewed publications. The work focuses on transparency, quality assurance, and performance monitoring for AI tools as they are introduced and refined in radiology workflows.
“This partnership allows us to accelerate our shared missions: to ensure that AI in radiology enhances, not compromises patient care,” said Dr. David B. Larson, co-director of the AIDE Lab and professor of radiology at Stanford University.
Research will take place within Stanford’s Department of Radiology, with participation from RP’s radiologists and data scientists. The goal is to define practical methods for AI oversight, including continuous monitoring protocols embedded in live clinical systems.
Radiology Partners serves over 3,400 healthcare facilities through its affiliated practices. MosaicOS, its proprietary cloud-native platform, supports AI-enabled imaging workflows across its network.
