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!

In 2021, one in every 100 kids aged 12-17 had an opioid use disorder (OUD). Medication treatment for OUD (MOUD) is effective, but only 3-4% of youth who need it receive treatment. This session will outline features of youth treatment for OUD, evidence for treating youth with medications, youth MOUD uptake and retention, the role of families in care, and opportunities for interdisciplinary care teams in outpatient settings. In this extended session, Dr. Marc Fishman (Mountain Manor & Johns Hopkins) will also facilitate interactive case reviews with attendees. If you have a case you would like to present, please let Bethany McLeman know: bethany.m.mcleman@dartmouth.edu Register here!