Clinical applications of AI in mental health care - Nature
Editors
Eric Kuhn, PhD
A National Center for PTSD Dissemination and Training Division and Stanford University, USA
Maurice D Mulvenna, PhD
Ulster University, UK
John B. Torous, MD MBI
Beth Israel Deaconess Medical Center, Harvard Medical School, USA
Raymond Bond, PhD
Ulster University, UK
Articles
Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being
Natural language processing system for rapid detection and intervention of mental health crisis chat messages
Natural language processing analysis of the psychosocial stressors of mental health disorders during the pandemic
An automatic speech analytics program for digital assessment of stress burden and psychosocial health
Systematic review of machine learning in PTSD studies for automated diagnosis evaluation
A conceptual framework of cognitive-affective theory of mind: towards a precision identification of mental disorders
Explainable artificial intelligence for mental health through transparency and interpretability for understandability
Complex modeling with detailed temporal predictors does not improve health records-based suicide risk prediction
Machine learning based suicide prediction and development of suicide vulnerability index for US counties
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npj Mental Health Research and npj Digital Medicine are collaborating on this joint collection to curate novel research that looks beyond feasibility to highlight how AI can be safely, ethically, and impactfully utilized to advance the understanding of mental illnesses and deliver better care to patients with these conditions. This collection will explore the latest advances and research in the newest generations of AI in mental health with a focus on clinically applicable, ethical, and transparent AI research.
While the first-generation AI applications for mental health developed in the 1960s and 1970s did not transform care, recent advances in computing, smartphones, algorithms, large language models, and data provide new opportunities for AI in mental health today. Advertisement
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Clinical applications of AI in mental health care
