Katharina Schultebraucks, PhD

When: Friday, February 21, 2025, 11am-12pm Eastern Time​​

​

Title: Integrating Digital Biomarkers and Large Language Models in Real-World Settings : How Digital Innovations and AI are Shaping the Future of Mental Health

​

Abstract: Digital technologies and advances in computational methods have become key drivers of innovation in medicine. In psychiatry, accurate and reliable digitally-derived measures of mental health can inform patient risk stratification and care. Digital psychiatry can reduce costs, increase scalability, and improve test-retest reliability while decreasing subjective biases. However, to date, digital technologies have not yet unlocked their full potential to guide clinical decision-making. In this talk, I will highlight examples of digital approaches in psychiatry from my own multidisciplinary research that is focused on bringing together the expertise of clinical practitioners with recent advances in predictive modeling and digital phenotyping.  I will discuss how those approaches bear a high potential for clinical application and may improve the scalability and sensitivity of clinical assessments. In view of these recent advancements in prognosticating risk using computational methods, I will conclude by discussing the clinical implications for the future use of machine learning approaches for predicting and monitoring posttraumatic stress and resilience in clinical settings.​

​

Bio: Dr. Schultebraucks completed her PhD in the Department of Psychiatry and Psychotherapy at the Charité – Universitätsmedizin Berlin in Germany and the Department of Psychology at the Free University, Berlin (degree: summa cum laude – graduate with honors). She did her postdoctoral fellowship in the Department of Psychiatry at NYU Grossman School of Medicine and was the Florence Irving Assistant Professor and Director of Computational Medicine and Artificial Intelligence in the Department of Emergency Medicine and Psychiatry at Columbia University before joining NYU in January 2023. She is currently Co-Director of the Computational Psychiatry Program and Associate Professor in the Department of Psychiatry and in the Division of Healthcare Delivery Science, Department of Population Health at NYU Grossman School of Medicine. Furthermore, she is an Associate Professor in Biomedical Engineering at NYU Tandon School of Engineering. Moreover, she is an Investigator in the Neuroscience Institute at NYU Grossman School of Medicine as well as a Center Affiliated Investigator at the Constance and Martin Silver Center on Data Science and Social Equity at NYU Silver School of Social Work. Dr. Schultebraucks investigates longitudinal and prospective studies to identify complex sets of early predictors. Her primary research focus is centered on precision psychiatry by applying advanced computational methods to improve individualized risk stratification and individualized treatment allocation, leading to publications in Nature Medicine, JAMA Psychiatry, and Molecular Psychiatry as the first and corresponding author. It is important to ensure the accuracy and generalizability of machine learning algorithms, in particular, whether the algorithm provide fair and equitable risk stratification and treatment assignment. Dr. Schultebraucks has been awarded several awards and national and international grants, e.g., she is currently the PI of two R01s funded by the National Institutes of Health (NIH) and PI of a multicenter grant funded by the Swiss National Science Foundation.​​​​​​​​