The CTN Youth SIG is pleased to invite you to an upcoming presentation/webinar: Scaling out evidence-based interventions through primary care settings: A Case Study of the Pathways for African Americans (PAAS) Program Presenter: Velma McBride Murry, PhD Lois Autrey Betts Endowed Chair Co-Director, Vanderbilt University Medical Center Program for Health Equity Research Distinguished University Professor, Departments of Health Policy & Human and Organizational Development, Vanderbilt University This presentation will highlight the role of primary care setting as a conduit for evidence-based intervention access for families. In addition, attention will be given the need to focus on system level interventions to reduce the need for families and youth to be “resilient.” About the presenter: Dr. Velma McBride Murry holds the Lois Autrey Betts Endowed Chair, previously held an appointed position of Associate Provost, Research and Innovation, currently serves as Co-Director of the Vanderbilt University Medical Center Program for Health Equity Research (PHER), and is a University Distinguished Professor in Departments of Health Policy and Human and Organizational Development . She is Past President of the Society for Research on Adolescence and current President of The International Consortium of Developmental Science Societies. McBride Murry is one of the 100 elected members to the 2020 Class of the National Academic of Medicine. She is an appointed standing member of National Institutes of Health National Advisory Mental Health Research Council. Her research examines the significance of context to everyday life experiences of African American families and youth, focusing on processes through which racism, and other social structural stressors, cascade through families to influence parenting and family functioning, developmental outcomes, and adjustment among youth, during critical developmental periods from middle childhood through young adulthood. Register here!  

Join the Translation and Implementation SIG on November 19th for their next session, titled, "Implementation Outcomes in NIDA Clinical Trials Network Studies: A Systematic Review of Studies Conducted Over 25 Years in the CTN."

Join the NIDA CCTN on November 20 (12pm ET) for a webinar on the future of AI in medicine! Presenter: Dr. Curt Langlotz Professor of Radiology, Medicine, and Biomedical Data Science Senior Associate Vice Provost for Research Director, Center for Artificial Intelligence in Medicine & Imaging Senior 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. Learning objectives 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 efficiency of medical image interpretation. He also serves as Senior Fellow at Stanford’s Institute for Human-Centered Artificial Intelligence and Director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center), which supports over 200 faculty at Stanford who pursue interdisciplinary machine learning research to improve clinical care. Register here!