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Mental disorders and their sequelae are leading causes of disability worldwide. Despite advances in neuroscience, genetics, and therapeutic interventions, psychiatric practice has lagged many other fields of medicine in reducing the prevalence and burden of illness. Treatment selection remains a trial-and-error process and few tools are available for the prevention of morbidity and mortality associated with mental illness. The growing availability of large-scale data resources coupled with advances in AI/ML are providing new opportunities to address these gaps and enable the growth of “precision psychiatry”. This presentation will explore these emerging opportunities using the domain of suicide prevention as a primary example. We will discuss the prospects and pitfalls of efforts to apply AI/ML for prediction, prevention, and treatment of psychiatric outcomes and for enhancing clinical decision-making.
