Artificial Intelligence for Mental Health and Mental Illnesses: An Overview

By Sarah Graham, Colin A. Depp, Ellen E. Lee, Camille Nebeker, X. Tu, Ho-Cheol Kim, Dilip Jeste. Current Psychiatry Reports, 2019

736 citations recorded by the source corpus.

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. 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.

Subfields: Applied Psychology, Artificial Intelligence, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/c041af0e08ccd1afb6b94e17e1da2539f5b34b7a

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Citations in the corpus

Revolutionizing healthcare: the role of artificial intelligence in clinical practice (2023, methodology): “The current published studies addressing the applicability of AI in mental health concluded that depression is the most commonly investigated mental disorder [88].”
The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century (2024, background): “By monitoring patterns in speech [51], behavior [52], and social media activity [53,54], AI tools can help in identifying the onset of mental health issues and suggest interventions tailored to the individual’s unique situation.”
Leveraging Large Language Models for Mental Health Prediction via Online Text Data (2023, background): “…range of opportunities for UbiComp, Human-Computer Interaction (HCI), and mental health communities, such as online public health monitoring systems [33, 62], intelligent assistants for mental counselors and supporters [74, 75], mental-health-aware personal chatbots [4, 28], to just name a few.”
AI in mental health. (2020, background): “The ﬁeld of mental health care, like a range of other ﬁelds, has been impacted by the revolution in digital technology and artiﬁcial intelligence (AI), with the ﬁeld of digital mental health now ﬁrmly established as well as continued and emerging work on AI-driven solutions for mental health [1].”
Artificial Intelligence for Mental Healthcare: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom (2021, methodology): “The researchers assess the participants using wearable sensors, clinical, neuropsychological, and physical functioning measures, and microbiome-based biomarkers, with the ultimate goal of identifying the earliest predictors of cognitive and functional decline (10,36,125).”