Artificial Intelligence for Mental Health and Mental Illnesses: an Overview


Purpose of Review

Artificial intelligence (AI) technology holds both great promise to transform mental healthcare and potential pitfalls. This article provides an overview of AI and current applications in healthcare, a review of recent original research on AI specific to mental health, and a discussion of how AI can supplement clinical practice while considering its current limitations, areas needing additional research, and ethical implications regarding AI technology.

Recent Findings

We reviewed 28 studies of AI and mental health that used electronic health records (EHRs), mood rating scales, brain imaging data, novel monitoring systems (e.g., smartphone, video), and social media platforms to predict, classify, or subgroup mental health illnesses including depression, schizophrenia or other psychiatric illnesses, and suicide ideation and attempts. Collectively, these studies revealed high accuracies and provided excellent examples of AI’s potential in mental healthcare, but most should be considered early proof-of-concept works demonstrating the potential of using machine learning (ML) algorithms to address mental health questions, and which types of algorithms yield the best performance.


As AI techniques continue to be refined and improved, it will be possible to help mental health practitioners re-define mental illnesses more objectively than currently done in the DSM-5, identify these illnesses at an earlier or prodromal stage when interventions may be more effective, and personalize treatments based on an individual’s unique characteristics. However, caution is necessary in order to avoid over-interpreting preliminary results, and more work is required to bridge the gap between AI in mental health research and clinical care.

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Fig. 1


Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

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This study was supported, in part, by the National Institute of Mental Health T32 Geriatric Mental Health Program (grant MH019934 to DVJ [PI]), the IBM Research AI through the AI Horizons Network IBM-UCSD AI for Healthy Living (AIHL) Center, by the Stein Institute for Research on Aging at the University of California San Diego, and by the National Institutes of Health, Grant UL1TR001442 of CTSA funding. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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Correspondence to Dilip V. Jeste.

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Conflict of Interest

Sarah Graham, Xin Tu, and Ho-Cheol Kim each declare no potential conflicts of interest.

Colin Depp and Dilip V. Jeste are Co-Directors of UCSD-IBM Center on Artificial Intelligence for Healthy Living (2018–2022). This is a grant to UCSD from IBM. Drs. Depp and Jeste have no commercial interest in IBM or any other AI-related companies.

Ellen E. Lee has received grants from The National Institute of Mental Health, The National Institutes of Health, and The Stein Institute for Research on Aging.

Camille Nebeker is a co-investigator on a grant supported by IBM and her research on the ethics of emerging technologies is supported by the Robert Wood Johnson Foundation.

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Graham, S., Depp, C., Lee, E.E. et al. Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. Curr Psychiatry Rep 21, 116 (2019).

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  • Technology
  • Machine learning
  • Natural language processing
  • Deep learning
  • Schizophrenia
  • Depression
  • Suicide
  • Bioethics
  • Research ethics