Current psychiatry reports · 2025
The Use of Artificial Intelligence for Personalized Treatment in Psychiatry.
СтатьяОбзорНазвание и аннотация: Перевод в очереди
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Jalali Sara, You Qiong, Xu Victoria, Chen Langfan, Zini Jonathan, Zamorano Tihare, Luo Yangyu, Friesen Timothy, Fontana Gabriel James, Benrimoh David
Аннотация
PURPOSE OF REVIEW: This review examines the role of artificial intelligence (AI) in psychiatry in the past 5 years across four domains: screening; outcome prediction; risk and relapse prediction; and psychotherapy.
RECENT FINDINGS: Machine learning models applied to questionnaires, electronic health records, neuroimaging, and digital phenotyping data demonstrate promising results for predicting symptom trajectories, relapse risk and treatment response, but external and clinical validation is rare. Randomized controlled trials provide some evidence for AI-enabled clinical decision support, but only preliminary evidence for chatbot-delivered psychotherapy. Some preliminary evidence for chatbots in screening exists. Ethical risks, including automation bias, model opacity and socioemotional harms, complicate integration into practice. Current evidence only supports AI's role as a complement to clinical expertise. To realize safe integration of AI into clinical practice, future work should focus on prospective, multi-site trials with active comparators, external validation across diverse populations, transparent reporting, and governance frameworks that prioritize explainability, oversight, and equity.
RECENT FINDINGS: Machine learning models applied to questionnaires, electronic health records, neuroimaging, and digital phenotyping data demonstrate promising results for predicting symptom trajectories, relapse risk and treatment response, but external and clinical validation is rare. Randomized controlled trials provide some evidence for AI-enabled clinical decision support, but only preliminary evidence for chatbot-delivered psychotherapy. Some preliminary evidence for chatbots in screening exists. Ethical risks, including automation bias, model opacity and socioemotional harms, complicate integration into practice. Current evidence only supports AI's role as a complement to clinical expertise. To realize safe integration of AI into clinical practice, future work should focus on prospective, multi-site trials with active comparators, external validation across diverse populations, transparent reporting, and governance frameworks that prioritize explainability, oversight, and equity.