Big Data, AI and Precision Psychiatry: Advancing Prevention and Treatment-View Presentation

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Dr. Jordan Smaller, a psychiatrist, epidemiologist, and geneticist, has shared insights on the integration of big data and AI in precision psychiatry. He highlighted that psychiatric disorders are common and often lead to significant morbidity and mortality, yet effective treatments can be elusive. Dr. Smaller emphasized the importance of precision medicine, which leverages genetic, environmental, and lifestyle variability to improve disease treatment and prevention.<br /><br />He detailed various advancements, such as using electronic health records (EHRs) and AI to predict disorders like bipolar disorder and suicide risk. His research demonstrates that EHR data can accurately predict psychiatric conditions and outcomes, potentially enhancing clinical decision-making and prevention strategies.<br /><br />Dr. Smaller also discussed the use of polygenic risk scores as psychiatric biomarkers. These scores, derived from genomic data, could help identify genetic predispositions to disorders like schizophrenia and depression, although their practical application is still developing.<br /><br />Furthermore, Dr. Smaller underscored the potential of pharmacogenetics and machine learning in tailoring psychiatric treatments. He highlighted the possibilities presented by digital health technologies and algorithms to provide real-time, personalized interventions.<br /><br />The challenges discussed include addressing algorithmic bias, integrating these technologies into clinical practice responsibly, and ensuring ethical use to prevent stigma. Overall, Dr. Smaller envisions a future where innovative data-driven approaches significantly enhance the precision and efficacy of psychiatric care.

Keywords

precision psychiatry

big data

AI

psychiatric disorders

precision medicine

electronic health records

polygenic risk scores

genetic predispositions

pharmacogenetics

machine learning

digital health technologies

algorithmic bias