The performance of artificial intelligence-driven technologies in diagnosing mental disorders: an umbrella review

Abstract

Artificial intelligence (AI) has been successfully exploited in diagnosing many mental disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI models in diagnosing different mental disorders. This umbrella review aims to synthesize results of previous systematic reviews on the performance of AI models in diagnosing mental disorders. To identify relevant systematic reviews, we searched 11 electronic databases, checked the reference list of the included reviews, and checked the reviews that cited the included reviews. Two reviewers independently selected the relevant reviews, extracted the data from them, and appraised their quality. We synthesized the extracted data using the narrative approach. We included 15 systematic reviews of 852 citations identified. The included reviews assessed the performance of AI models in diagnosing Alzheimer’s disease (n = 7), mild cognitive impairment (n = 6), schizophrenia (n = 3), bipolar disease (n = 2), autism spectrum disorder (n = 1), obsessive-compulsive disorder (n = 1), post-traumatic stress disorder (n = 1), and psychotic disorders (n = 1). The performance of the AI models in diagnosing these mental disorders ranged between 21% and 100%. AI technologies offer great promise in diagnosing mental health disorders. The reported performance metrics paint a vivid picture of a bright future for AI in this field. Healthcare professionals in the field should cautiously and consciously begin to explore the opportunities of AI-based tools for their daily routine. It would also be encouraging to see a greater number of meta-analyses and further systematic reviews on performance of AI models in diagnosing other common mental disorders such as depression and anxiety.

Advancing Mental Health Care with AI-Enabled Precision Psychiatry Tools: A Patent Review

“…Potential explanations for this trend include the troubles associated with the high dimensionality and heterogeneity of genetic data, which necessitates the collection of large datasets and the usage of complex modelling techniques (Su et al, 2020). Additionally, it has been demonstrated that neuroimaging data produces more accurate classifiers than genetic data for diagnosing a variety of mental disorders (Abd-alrazaq et al, 2022;Su et al, 2020). Another potential explanation may be the maturity of the technology for deriving diagnoses based on genetic data relative to more recent advances, such as the utilization of behavioral data gathered through mobile devices, which may have caused a transition to more modern and convenient data acquisition approaches (Bennett et al, 2012;McGuffin et al, 2004;Su et al, 2020).…”

Cracking the Code: A Scoping Review to Unite Disciplines in Tackling Legal Issues in Health Artificial Intelligence

“…Yet, others observe that insufficient regulatory oversight could undermine that aim and harm vulnerable users ("bots could be programmed to infiltrate people's homes and lives en masse, befriending children and teens, influencing lonely seniors, or harassing confused individuals until they finally agree to services that they otherwise would not have chosen." [36][37] These debates underscore the need for crossdisciplinary input, including from those whose lives are affected by health-AI, to achieve equitable and non-discriminatory health AI. [38] National and international leaders increasingly advocate for interdisciplinary collaboration on AI regulation.…”

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“…While not a substitute for professional diagnosis, these tools can complement professional care by guiding individuals towards help, assisting practitioners in monitoring progress, and contributing valuable research data. AI-powered assessments and chatbots play a role in modern mental healthcare, but their integration should always be part of a comprehensive approach that includes professional evaluation and treatment (Abd-alrazaq et al, 2022(Abd-alrazaq et al, , 2023Omarov et al, 2023;Rathnayaka et al, 2022).…”

“…The rapid advancement of artificial intelligence (AI) has significantly impacted various facets of life, including the field of mental health (Abd‐alrazaq et al., 2022; Wilson et al., 2023). Technologies such as machine learning, natural language processing, and data analytics have become integral to digital mental health interventions, encompassing virtual therapy programs and even self‐diagnosis tools (Aboueid et al., 2019; Omarov et al., 2023; Rathnayaka et al., 2022; Su et al., 2020).…”

“…Technologies such as machine learning, natural language processing, and data analytics have become integral to digital mental health interventions, encompassing virtual therapy programs and even self‐diagnosis tools (Aboueid et al., 2019; Omarov et al., 2023; Rathnayaka et al., 2022; Su et al., 2020). This growing AI influence extends to both assisting professionals in diagnosing mental illnesses and analysing large datasets to uncover trends and predict treatment outcomes (Abd‐alrazaq et al., 2022, 2023).…”