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.

Fig. 1. Flow chart of the study selection process: 852 citations were retrieved from searching the databases.

Of these, 344 duplicates were removed. Screening titles and abstracts of the remaining citations led to excluding 446 citations. By reading the full text of the remaining 62 publications, we excluded 48 publications. An additional systematic review was identified by checking the list of the included reviews. In total, 15 systematic reviews were included in the current.

Fig. 2. Review authors’ judgments about each appraisal item: The quality of the included reviews was assessed against appraisal items.

Yes (green) refers that study meets the item, thereby, it has a good quality in terms of that item. No (red) refers that study did not meet the item, thereby, it has poor quality in terms of that item. Unclear (yellow) refers that we could not appraise the study in terms of the item due to the lack of reported information. Not applicable (gray) refers that the appraisal item is not applicable to the systematic review as it does not include a feature that the item assesses.