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Artificial Intelligence
RSNA leads radiology in the practical, ethical application of AI—advancing intelligent patient care through education, research, data curation and collaboration.
Radiology’s Trusted Source
RSNA is leading radiology forward—delivering trusted AI education, supporting peer-reviewed research, developing ground-truth datasets, and creating dynamic opportunities for collaboration.
Research
All six RSNA journals cover the application of emerging technologies in radiology. Explore AI’s impact on diagnostics and patient care.
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Data
RSNA fosters the development of ground-truth datasets to advance the field of AI in medical imaging and support continued progress.
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Collaboration
RSNA’s annual meeting provides unmatched opportunities to contribute and connect with industry trailblazers and like-minded professionals from around the world.
Explore AI at the Annual MeetingAI Journal Highlights
RSNA delivers cutting-edge research and practical guidance on the role of AI in medical imaging through six peer-reviewed journals.
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Roadmap: An Ontology of Medical AI Models and Datasets
ROADMAP provides a machine‑readable ontology to describe medical AI models and datasets, improving transparency, interoperability, detection and reuse.
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Machine Learning Using Clinical and Cardiac MRI Features to Predict Long-term Outcomes in Acute STEMI
A machine learning model combining cardiac MRI and clinical data improves long-term prediction of major adverse cardiovascular events after STEMI care.
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Deep Learning Models Connecting Images and Text: A Primer for Radiologists
Advances in deep learning now link medical images with text, enabling multimodal models that can generate reports, caption images, and streamline radiology workflows to improve diagnostic accuracy.
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Deep learning-based pulmonary nodule risk assessment outperforms established malignancy risk scores in lung cancer screening
A deep learning model improves pulmonary nodule malignancy risk estimation in lung cancer screening, outperforming Lung‑RADS and PanCan models overall.
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AI Do’s and Don’ts in Cancer Imaging: Remembering the Patient Behind the Pixel
A reflection from RSNA 2025 highlights patient‑centered AI in cancer imaging, sharing lessons, pitfalls and best practices for responsible research.
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Deep Learning Segmentation of Pectoralis Muscle Volume at CT and Comparison with Pectoralis Muscle Area in COPD
A deep learning CT model accurately segments pectoralis muscle volume, showing stronger reproducibility and associations with COPD outcomes overall.
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AI Thought Leaders Share Their Perspectives
"Building collaborative, intelligent connections between people and technology will energize the care team and redefine how radiologists deliver care."
Curtis P. Langlotz, MD, PhD
RSNA President 2023–2024
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AI Thought Leaders Share Their Perspectives
"The fusion of radiology and AI presents a unique opportunity to significantly boost accuracy and efficiency."
Nina Kottler, MD, MS
Associate Chief Medical Officer for Clinical AI at Radiology Partners
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AI Thought Leaders Share Their Perspectives
"With AI having come to a level at par with human authors, the use of chatbots and natural language processing have the potential to revolutionize the field of medical writing."