Artificial Intelligence for Mental Health and Mental Illnesses: an Overview

Abstract

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.

Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality

“…This scientific discipline stems from Artificial Intelligence (AI), i.e., a computer science field performing tasks capable of emulating human performance, generally learning to understand complex data, an endeavor that requires human intelligence ( Bawack, 2019 ; Wang, 2019 ; Graham et al, 2020 ). ML algorithms have progressively gained popularity for several reasons, including their ability to automatically learn the inherent structure of a dataset ( Kononenko, 2001 ; Abu-Mostafa et al, 2012 ; Facal et al, 2019 ) without requiring a priori hypotheses about relationships between variables ( Miotto et al, 2017 ; Vieira et al, 2017 ; Graham et al, 2019 , 2020 ). Conversely, ML algorithms can discover and predict data trends and patterns by building on existing information and highlight unexpected relationships between variables ( Vieira et al, 2017 ; Graham et al, 2019 , 2020 ).…”

Clinical Information Systems – Seen through the Ethics Lens

“…code status, advance directive [23], and clinical ethics consultation [24]. In contrast, AI and ML procedures were partly described at more length in an overview style [25][26][27]. When devices were referred to in the studies, they were dealt with as a cluster of technologies, i.e.…”

“…Alongside the algorithms, all papers on AI and ML addressed the paramount importance of the data sets on which the algorithms were trained. Low quality, inherent biases (e.g., due to under-represented groups), and subjectivity in clinical notes led to the amplification of errors on a large scale and very often to disparities and discrimination of disadvantaged groups [25,26,35,36]. In turn, clean, reliable, and valid data would bring AI into the position to mitigate injustice in decision making arising from human preconceptions [37].…”

“…Ashton and Sullivan [55] Baldini et al [38] Boers et al [36] Bourla et al [45] Brill et al [49] Brisson et al [33] Carter et al [35] Davenport and Kalakota [25] De Riel et al [31] Duckett [29] Eberlin et al [52] Erikainen et al [28] Evans and Whicher [47] Galvin et al [48] Gensheimer et al [54] Gooding [46] Graham et al [26] Ho and Quick [57] Lenca et al [56] Kogetsu et al [21] Kuhnel [53] Laurie [58] Lehmann et al [23] Loftus et al [37] Macdonald et al [50] Mars et al [51] McBride et al [40] McWilliams et al [20] Meredith et al [30] Moscatelli et al [22] Musher et al [39] Natsiavas et al [43] Pathak and Chou [44] Rashidi et al [27] Robichaux et al [41] Sánchez et al [34] Sanelli-Russo et al [24] Stockdale et al [42] Wilburn [32] [56] provided an overview of ethical themes grouped as families and subfamilies. There was a preponderance of studies found on independence and safety considerations.…”

Artificial intelligence in prediction of mental health disorders induced by the COVID-19 pandemic among health care workers

“…In such circumstances, one of the greatest impacts of digital psychiatry, particularly applied artificial intelligence (AI) and machine learning (ML) (10)(11)(12)(13)(14)(15) during the ongoing COVID-19 pandemic, is their ability of early detection and prediction of HCWs' mental health deterioration, which can lead to chronic mental health disorders. Further-more, AI-based psychiatry may help mental health practitioners redefine mental illnesses more objectively than is currently done by DSM-5 (14). Regardless of the specific application, ie, prediction, prevention, or diagnosis, AI-based technologies in psychiatry rely on the identification of specific patterns within highly heterogeneous multimodal sets of data (13).…”