We are pleased to announce that Curtis P. Langlotz, MD, PhD, Professor of Radiology, Medicine and Biomedical Data Science at Stanford University, will deliver our anniversary webinar in our FUTURE-AI webinar series.
Date: 12 February 2026
Time: 15.00-16.00 GMT | 16.00-17.00 CET
Location: Online – Download the Zoom calendar invite here.
Talk Title: “The Future of AI and the Radiology Workforce ”
Abstract
Artificial intelligence (AI) is an incredibly powerful tool for building systems that support the work of radiologists. The analysis of digital images led to the earliest progress developing machine learning methods to support healthcare decision making. This sparked high interest and explosive growth in the use of AI and machine learning methods to analyze medical imaging data. These promising techniques create systems that perform some diagnostic tasks at the level of expert radiologists. The systems have the potential to provide real-time assistance to radiologists, thereby reducing diagnostic errors, detecting disease early, improving patient outcomes, and reducing costs. We will review the origins of AI and its applications to medical imaging, define key terms, and show examples of real-world applications that suggest how AI and large language models may change the practice of medicine. We will also review key shortcomings and challenges for AI that may limit the application of these new methods. Finally, we will present a prediction model for how AI will affect the radiology workforce in the next 5 years.
About the speaker
Dr. Langlotz is a Professor of Radiology, Medicine, and Biomedical Data Science, a Senior Fellow at the Institute for Human-Centered Artificial Intelligence, and Senior Associate Vice Provost for Research at Stanford University. He also serves as Director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center), which supports over 250 faculty at Stanford who conduct interdisciplinary machine learning research to improve clinical care. Dr. Langlotz’s NIH-funded laboratory develops machine learning methods to eliminate diagnostic errors and detect disease early. He has led many national and international efforts to improve medical imaging, including the RadLex standard terminology system and the Medical Imaging and Data Resource Center (MIDRC), a U.S. national imaging research resource.