Join the CTN Youth SIG for their next webinar, “Age, Period, and Cohort Effects in Alcohol and Other Substance Use: Implications for Health and Prevention,” presented by Katherine M. Keyes, PhD (Columbia University), on Friday, January 17, 2025, 11am-12pm ET. Incidence, prevalence, and persistence of substance use differ by generation and by substance. Understanding how each generation uses alcohol and other drugs, why, and how health is implicated is critical for public health. The presentation will overview what we know about current trends in substance use, how rates are organized by age, period, and cohort, and how these trends are shaping public health overall. About the presenter Katherine M. Keyes, PhD, is Professor of Epidemiology at the Columbia University Mailman School of Public Health. Dr. Keyes’ research focuses on psychiatric and substance use epidemiologyacross the life course, including early and cross-generational origins of child and adult health and cohort effects on substance use, mental health, and injury outcomes including suicide and overdose. She is the author of more than 450 peer-reviewed publications, and two textbooks. Register here!

A recording of this webinar, held on November 20, 2024, is now available! (passcode: N7A3h^SX) Presenter:Dr. Curt LanglotzProfessor of Radiology, Medicine, and Biomedical Data ScienceSenior Associate Vice Provost for ResearchDirector, Center for Artificial Intelligence in Medicine & ImagingSenior Fellow, Institute for Human-Centered Artificial Intelligence Artificial intelligence (AI) is an incredibly powerful tool for building systems that support the work of clinicians and researchers. Over the last decade, machine learning methods have revolutionized the analysis of medical data, leading to high interest and explosive growth in the use of AI and machine learning methods. These promising techniques create systems that perform some clinical tasks at the level of expert physicians. Deep learning methods in imaging 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 medicine, and medical imaging, define key terms, and show examples of real-world applications that suggest how AI may change the practice of medicine.  We will also review key shortcomings and challenges that may limit the application of these new methods. About the presenter Curtis P. Langlotz, MD, PhD : Dr. Langlotz is Professor of Radiology, Medicine, and Biomedical Data Science, and Senior Associate Vice Provost for Research at Stanford University. His NIH-funded laboratory develops machine learning methods to improve the accuracy and…