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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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Collaborative Integration of AI and Human Expertise to Improve Detection of Chest Radiograph Abnormalities
Collaborative AI integrates eye-tracking and report data to reduce perceptual errors and improve diagnostic accuracy in chest radiograph interpretation.
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TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI
To develop and evaluate an automated MRI segmentation model for robust segmentation of major anatomic structures independent of MRI sequence.
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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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Decoding large language models for radiology: strategies for fine-tuning and prompt engineering
Large language models are transforming radiology by automating reporting and research tasks, but safe use requires fine-tuning and prompt optimization to reduce errors and ensure reliable clinical performance.
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Improving Clinically Significant Prostate Cancer Detection with a Multimodal Machine Learning Approach: A Large-Scale Multicenter Study
To develop and prospectively validate a clinical and radiologic model to predict clinically significant prostate cancer (csPCa) using biparametric MRI (bpMRI).
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Impact of Deep Learning–based Artificial Intelligence Assistance on Reader Agreement in Coronary CT Angiography Interpretation
To evaluate the impact of a fully automated, multitask deep learning (DL) algorithm on interreader agreement of coronary artery disease (CAD) detection and stenosis classification using coronary CT angiography (CCTA).
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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."