For radiologists especially, artificial intelligence (AI) is no longer just over the horizon; it’s in the reading room, right now. This practical immediacy is precisely the premise behind the 2026 ARRS Annual Meeting Categorical Course, Clinical Artificial Intelligence in Radiology. Presented live and virtually from the David L. Lawrence Convention Center in Pittsburgh, PA, this two-day ARRS Categorical Course continues our 125-year-old legacy of forward-looking education by arming radiologists with a robust understanding of how AI is reshaping the specialty.
Dr. Shandong Wu | Cat Course Codirector
Clinical Artificial Intelligence in Radiology brings together more than 20 distinguished faculty from leading institutions across the globe, all led by Shandong Wu, PhD, founding director of the University of Pittsburgh’s Center for AI Innovation in Medical Imaging, a cross-campus initiative including more than 130 researcher and clinician members. Also a professor of radiology, biomedical informatics, bioengineering, intelligent systems, clinical and translational science, one of Dr. Wu’s ARRS Cat Course codirectors is an abdominal radiologist and director of diagnostic AI at the University of Washington, Yee Seng Ng, MD.
Dr. Shandong Wu | Cat Course Codirector
Alongside codirector and 2024 AJR Lee F. Rogers International Fellow in Radiology Journalism Hyun Soo Ko, MD (Peter MacCallum Cancer Centre, Australia), the trio is curating a curriculum of more than two dozen lectures and panels across seven thematic sections, giving registrants a comprehensive view of AI’s current and future roles in everyday practice.
Dr. Hyun Soo Ko | Cat Course Codirector
SUN, APRIL 12—From Concept to Clinic: Building AI Literacy
Day one of Clinical Artificial Intelligence in Radiology kicks off with “Getting to Know AI,” a primer tailored for all levels of experience. Tessa Cook, MD, PhD (University of Pennsylvania), provides an overview of radiological progress in AI, while Dr. Ko demystifies essential concepts, such as machine learning, deep learning, radiomics, as well as generative and agentic AI.
Dr. Linda Moy | Vice Chair of AI, NYU Radiology
Up next, inaugural vice chair of AI at New York University’s radiology department, Linda Moy, MD, will provide an invaluable look into leveraging AI to improve workflow efficacy and effectiveness alike. Dr. Wu himself closes the Cat Course’s first session. The leader of Pittsburgh’s Intelligent Computing for Clinical Imaging lab will explore and explain how AI is enhancing imaging interpretation for computational insights—from screening and triage to diagnosis and prediction.
Clinical Implementation: From Regulation to Real-World Deployment
Section two of Clinical Artificial Intelligence in Radiology, “AI Clinical Implementation,” addresses legal, regulatory, and operational frameworks essential for radiologists seeking to implement or evaluate AI tools in practice. Didactic highlights will include guidance on U.S. Food and Drug Administration (FDA) regulations and performance monitoring by Melissa Davis, MD, MBA (Yale), as well as insights into distinguishing high-quality AI models from market hype.
In a uniquely insightful presentation, Julian Rivera, JD (University of Pittsburgh), will tackle the legion of legal considerations accompanying AI adoption: liabilities, ethical perspectives on signing contracts, collaborative business modes with AI companies, etc. Dr. Cook returns to share her expertise on evaluating local versus commercial solutions when measuring ROI, while a panel moderated by Dr. Moy will outline best practices and common pitfalls.
Beyond the Pixel: Multimodality and Multidimensional AI
The promise of any good AI expands significantly when paired with non-image data. The “Going Beyond Images to Multimodality” session explores emerging applications that leverage large language models, vision-language models, and foundation models. Presenters Heather Whitney, PhD (University of Chicago), and Lifeng Yu, PhD (Mayo Clinic), will delve into data curation, federated learning, and the physics of AI model performance. With Christian Bluethgen, MD (University Hospital of Zurich), having assessed multimodal data methodologies in his presentation, a panel discussion on tackling technical challenges to find opportunities rounds out day one of this ARRS Cat Course.
MON, APRIL 13—Practical Impact Across Subspecialties
AI’s reach across subspecialties is the focus on Monday. Presenters including Constance Lehman, MD, PhD (Harvard), Ali Guermazi, MD, PhD (Boston University), and 2022 ARRS Gold Medalist Edward Y. Lee, MD, MPH (Harvard) will detail AI tools in breast, musculoskeletal, pediatric imaging, respectively. Dr. Ng’s highly anticipated survey of AI and abdominal imaging will be followed by a lecture from neuroradiologist Paulo De Aguiar Kuriki, MD (UT Southwestern).
Dr. Edward Y. Lee | ARRS Gold Medalist
That’s not all either. Real-world cardiothoracic, interventional, and nuclear medicine cases will further demonstrate how AI is already reflowing imaging workloads, improving diagnostic accuracy, and personalizing care across organ systems and patient populations.
Shaping Tomorrow: Research, Education, and Ethical Engagement
Day two of Clinical Artificial Intelligence in Radiology continues with “AI Research and Education,” including a model development demonstration by Dooman Arefan, PhD (University of Pittsburgh), and an exploration of MD–PhD collaboration opportunities from Dr. Wu. Justin Peacock, MD, PhD (Uniformed Services University), will discuss educational roadmaps and training resources, addressing a key concern for attendees seeking to build or deepen their AI competencies.
This 2026 ARRS Annual Meeting Categorial Course concludes with “Humanity and AI,” a thought-provoking session covering radiologist–AI collaboration, fairness and bias, and imaging’s ever-evolving role in AI-powered services. Florence Doo, MD (University of Maryland) will help us find a foothold in our present human–AI ecosystem, followed by a warning for all the disparities AI run amok could actually exacerbate care of Judy Gichoya, MD, MS (Emory). Eduardo Mortani-Barbosa, MD, MBA (University of Pennsylvania), will then detail specific skill sets that AI-forward radiologists will need to hone in their practices and in their communities. Finally, ARRS Scholar and Gold Medalist and editor of Radiology: Artificial Intelligence Charles E. Kahn, MD (University of Pennsylvania), joins to facilitate a panel discussion on action items and what to do next.
Dr. Charles E. Kahn | Editor, Radiology: Artificial Intelligence
With each live lecture accompanied by an e-book chapter, Clinical Artificial Intelligence in Radiology will provide strategic context and tactical guidance for imagers of each practice type and at every level of training.
And as Dr. Wu tells InPractice, “AI in radiology is not just a technical shift—it’s a cultural one. This ARRS Categorical Course is about empowering radiologists to shape that future, not just react to it.”
With content spanning conceptual foundations to the most practical of pearls, the curriculum curated by Wu, Ng, Ko and colleagues this April is poised to be an essential learning experience for working radiologists looking to engage with AI at the frontlines of medical imaging care.