Patricia Conrod is a clinical psychologist and professor of Psychiatry and Addiction at Universite de Montreal, with over 20 years of experience conducting research on populations at risk of addiction. Her research has identified a number of psychological and biologic risk factors for addiction and has helped to delineate the motivational mechanisms that explain how risk translates to heavy or problematic substance misuse among vulnerable groups. Moreover, this research has led to the development of novel interventions that match the motivational basis of risk, supporting new, more personalized and targeted strategies for addiction treatment and prevention. This new approach involves intervening at the personality and neurocognitive level rather than at the symptom level and has proven remarkably effective at, not only reducing and preventing substance misuse, but also at reducing mental health problems such as anxiety, depression, suicidal ideation and conduct problems. Dr. Conrod has developed screening scales and intervention material that have been translated and tested in numerous languages and contexts around the world. She helped to design the IMAGEN study and led the phenotyping workpackage for the IMAGEN Consortium, and with Dr. Hugh Garavan (UVM), she established the ENIGMA Addiction working group which aims to apply the ENIGMA approach towards data pooling across addiction neuroimaging studies. She Co-directs the FRQS Research Network on Suicide, Mood Disorders and Related Conditions (RQSHA), the Canadian Cannabis and Psychosis Research Team (CCPRT), and the Universite de Montreal Initiative on Brain and Mental Health.
Conference : Will AI as a preventive medicine tool be accepted and used?
Friday 6 may 2022, 13h45 - 14h30 — Amphi mauve
Thirty years of longitudinal cohort studies have shown that psychiatric conditions can be predicted with some level of accuracy. Childhood genetic, psychological and neurological markers of risk in interaction with environmental conditions can not only predict who is at risk, but what kinds of mental health and substance use problems an individual is likely to experience during adulthood. Machine learning models are further refining prediction tools and novel intervention strategies have been developed and tested targeting different risk pathways. Such targeted approaches have proven to be highly effective with lasting effects on adult mental health outcomes. However, is society ready to apply what we know about risk for mental health in order to prevent it? This talk will address challenges associated with AI-informed targeted prevention and the social, ethical and practical issues that can mitigate these concerns in order to render preventive mental health more acceptable and impactful.