NCT Data Science Seminar

The NCT Data Science Seminar is a campus-wide effort bringing together thought-leading speakers and researchers in the field of data science to discuss both methodological advances as well as medical applications.

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Upcoming & Recent Talks

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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. 

Upcoming & Recent Talks

A promotional image for a seminar titled "AI Methods to Predict the Risk of Cancer," featuring speaker Prof. Elihu D. Schwartz from Harvard Medical School. The event is scheduled for June 5, 2025, at 11:00 AM, hosted by Prof. Dr. Minta Z. Grunken.

BioQuant, INF 267, Lecture Hall SR041

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Bioskech

Chris Sander started his career as a theoretical physicist and then switched to theoretical biology, in part inspired by the first completely sequenced genome. He founded two departments of computational biology - at the EMBL in Heidelberg and Memorial Sloan Kettering Cancer Center in New York - and co-founded the research branch of the European Bioinformatics Institute in Cambridge and a biotech startup with Millennium in Boston.

Chris joined the Harvard community in 2016 as faculty in Cell Biology and then Systems Biology. Special Advisor for Quantitative Biology to the Ludwig Center at Harvard, and Associate Member of the Broad Institute. He is creating new connections between scientists at Dana-Farber and Harvard Medical School, including building translational collaborative bridges for scientists using quantitative sciences to solve biological problems.

With his group and collaborators, Chris aims to beat drug resistance in cancer using systems biology methods to develop combination therapies. They are also developing the next generation cBioPortal for cancer research and therapy, obtaining biomolecular structures and functional interactions on a large scale using evolutionary information, and adapting machine learning methods to mine millions of genomes. He is collaborating with groups in Denmark and the US to apply AI to longitudinal health records to identify patients at high risk for pancreatic and ovarian cancer and collaborates with clinicians in the design of effective affordable surveillance program aiming at the early detection of cancer.

Hosts

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    Prof. Dr. Lena Maier-Hein

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    Prof. Dr. Klaus Maier-Hein

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    Prof. Dr. Oliver Stegle

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