NCT Data Science Seminar

Das NCT Data Science Seminar ist eine campusweite Initiative, die führende Forscher im Bereich der Datenwissenschaft zusammenbringt, um methodische Fortschritte und medizinische Anwendungen zu diskutieren.

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5:00 PM

 

Abstract

Artificial intelligence (AI) is an incredibly powerful tool for building computer vision systems that support the work of radiologists. Over the last decade, artificial intelligence methods have revolutionized the analysis of digital images, leading to high interest and explosive growth in the use of AI and machine learning methods to analyze clinical images and text.  These promising techniques create systems that perform some image interpretation tasks at the level of expert radiologists.  Deep learning methods are now being developed for image reconstruction, imaging quality assurance, imaging triage, computer-aided detection, computer-aided classification, and radiology report drafting.  The systems have the potential to provide real-time assistance to radiologists and other imaging professionals, thereby reducing diagnostic errors, improving patient outcomes, and reducing costs.  We will review the origins of AI and its applications to medical imaging and associated text, 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 that may limit the application of these new methods.

 

Biosketch: Curtis P. Langlotz, MD, PhD

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 detect disease and eliminate diagnostic errors. 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. 

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    Prof. Dr. Klaus Maier-Hein

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    Prof. Dr. Oliver Stegle

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