RSNAI Monthly—February 2026 

The Radiological Society of North America (RSNA) leads the charge in advancing artificial intelligence (AI) as a tool to drive excellence in patient care and health care delivery. RSNAI Monthly is a collection of media briefs to help news outlets stay abreast of RSNA’s latest AI initiatives.

RSNA’s Annotated Library of AI Systems (ATLAS) now houses more than 230 model cards and dataset cards in 31 subspecialties, giving research communities the tools to make AI research more transparent, understandable and trustworthy. Researchers and developers can publish data cards that describe the key attributes of their AI models and datasets. Users can use ATLAS to discover, evaluate and compare these resources.

The Society for Imaging Informatics in Medicine’s National Imaging Informatics Course is an accelerated, week-long online experience—March 23 – 27—for radiology and pathology professionals, PACS administrators, vendor personnel and other professionals seeking expand their expertise in the field. Sponsored by RSNA and the Digital Pathology Association, the course is the first of its kind to delve into comprehensive fundamental principles in today’s real-world ecosystem of imaging informatics. There’s still time to register. 

In its February issue, RSNA’s flagship journal Radiology published guidelines for reporting studies of radiology large language models and commentary on agentic AI and human-AI collaboration in radiology. Original research explored super-resolution deep learning reconstruction for coronary CT angiography, a machine learning method to predict major adverse cardiovascular events in patients with ST-segment elevation myocardial infarction and how composition of a test set affects AI’s performance in detecting pediatric fractures.

RSNA’s journal Radiology: Cardiothoracic Imaging published research examining AI-derived high-risk features on coronary CT angiography and the feasibility of a deep learning system to fully automate systemic and pulmonary blood flow measurement with 4D flow MRI. A study in Radiology: Imaging Cancer evaluated how CT and MRI deep learning models trained on adult datasets could be personalized to segment pediatric hepatoblastoma, the most common liver cancer in children. 

With its suite of six premier journals, RSNA covers the breadth of groundbreaking research, career-advancing education, and specialty-focused techniques and trends for medical imaging professionals.

The Medical Imaging and Data Resource Center (MIDRC), funded by the National Institute of Biomedical Imaging and hosted at the University of Chicago, is co-led by RSNA, the American College of Radiology, and the American Association of Physicists in Medicine. Held on the third Tuesday of the month, the Seminar Series is an opportunity for members of the medical community at large to hear directly from the MIDRC team. The next session, planned for Tuesday, March 17, will feature research presentations from MIDRC investigators on new and noteworthy advances, and will include a live Q&A session for all attendees.  Free registration is required.