Artificial Intelligence for Mental Health and Mental Illnesses: an Overview

• Published in: Current psychiatry reports Vol. 21; no. 11; pp. 116 - 18
• Main Authors: Graham, Sarah, Depp, Colin, Lee, Ellen E., Nebeker, Camille, Tu, Xin, Kim, Ho-Cheol, Jeste, Dilip V.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-11-2019; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Electronic health records; Humans; Machine Learning; Medicine; Medicine & Public Health; Mental Disorders; Mental Health; Mental health care; Psychiatry; Psychiatry in the Digital Age (J Shore; Research ethics; Section Editor; Topical Collection on Psychiatry in the Digital Age; Deep learning; Technology; Machine learning; Schizophrenia; Bioethics; Depression; Research ethics; Natural language processing; Suicide
• ISSN: 1523-3812; 1535-1645
• DOI: 10.1007/s11920-019-1094-0

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.
Summary
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.