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SUMMARY:CTN Youth SIG Webinar: Scaling Out Evidence-Based Interventions Through Primary Care Settings: the PAAS Program
DESCRIPTION:The CTN Youth SIG is pleased to invite you to an upcoming presentation/webinar: \nScaling out evidence-based interventions through primary care settings: A Case Study of the Pathways for African Americans (PAAS) Program \nPresenter:\nVelma McBride Murry\, PhD\nLois Autrey Betts Endowed Chair\nCo-Director\, Vanderbilt University Medical Center Program for Health Equity Research\nDistinguished University Professor\, Departments of Health Policy & Human and Organizational Development\, Vanderbilt University \nThis 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.” \nAbout 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 [Vanderbilt School of Medicine] and Human and Organizational Development [Peabody College]. 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. \nRegister here! \n 
URL:https://ctnlibrary.org/event/ctn-youth-sig-webinar-dr-velma-murry/
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DTSTART;TZID=America/Los_Angeles:20241119T090000
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DTSTAMP:20260406T183924
CREATED:20241018T172922Z
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SUMMARY:CTN T&I SIG Webinar: Implementation Outcomes in NIDA Clinical Trials Network Studies
DESCRIPTION:Join the Translation and Implementation SIG on November 19th (9am-10am PT) 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.” \nThis webinar will be presented by Sophia Gonzalez\, MPH and Joe Glass\, PhD\, MSW of the CTN T&I Implementation Outcomes Workgroup and will focus on a study to explore and document the evolution of the inclusion of implementation outcomes across trials conducted within the CTN\, characterize outcomes included\, identify gaps and potential opportunities to strengthen implementation research within the CTN\, and inform the broader field of SUD treatment. \nContact Helene Chokron Garneau at hchokgar@stanford.edu if you would like to attend! \n 
URL:https://ctnlibrary.org/event/ctn-ti-sig-webinar-implementation-outcomes-in-nida-clinical-trials-network-studies/
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DTSTART;TZID=America/New_York:20241120T120000
DTEND;TZID=America/New_York:20241120T133000
DTSTAMP:20260406T183924
CREATED:20241113T005945Z
LAST-MODIFIED:20241113T005945Z
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SUMMARY:NIDA CTN - Future of AI in Medicine: Medical Imaging as an Example
DESCRIPTION:Join the NIDA CCTN on November 20 (12pm ET) for a webinar on the future of AI in medicine!\n\n \n\n \n\nPresenter:\nDr. Curt Langlotz\nProfessor of Radiology\, Medicine\, and Biomedical Data Science\nSenior Associate Vice Provost for Research\nDirector\, Center for Artificial Intelligence in Medicine & Imaging\nSenior Fellow\, Institute for Human-Centered Artificial Intelligence\n\n \n\nArtificial 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.\n\n \nLearning objectives\n \n\n 	Understand the origins of artificial intelligence and machine learning and their application to medical imaging\n \n 	Predict how machine learning methods will change the practice of medicine using current examples from medical imaging\n \n 	Describe how large language models will affect health care\n \n 	Assess the shortcomings of artificial intelligence that may limit its applicability\n\n \nAbout the presenter\n \n\nCurtis 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.\n\nRegister here!
URL:https://ctnlibrary.org/event/nida-ctn-future-of-ai-in-medicine-medical-imaging-as-an-example/
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