Abd-Alrazaq Alaa, Alhuwail Dari, Schneider Jens, Toro Carla T, Ahmed Arfan, Alzubaidi Mahmood, Alajlani Mohannad, Househ Mowafa
AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
Information Science Department, Kuwait University, Alshadadiya, Kuwait.
NPJ Digit Med. 2022 Jul 7;5(1):87. doi: 10.1038/s41746-022-00631-8.
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.
人工智能（AI）已成功应用于多种精神障碍的诊断。众多系统评价总结了关于人工智能模型诊断不同精神障碍准确性的证据。本伞状评价旨在综合以往关于人工智能模型诊断精神障碍性能的系统评价结果。为了识别相关的系统评价，我们检索了11个电子数据库，检查了纳入评价的参考文献列表，并查看了引用纳入评价的文献。两名评价者独立选择相关评价，从中提取数据并评估其质量。我们采用叙述性方法综合提取的数据。我们纳入了对852篇文献进行的15项系统评价。纳入的评价评估了人工智能模型在诊断阿尔茨海默病（n = 7）、轻度认知障碍（n = 6）、精神分裂症（n = 3）、双相情感障碍（n = 2）、自闭症谱系障碍（n = 1）、强迫症（n = 1）、创伤后应激障碍（n = 1）和精神障碍（n = 1）方面的性能。人工智能模型在诊断这些精神障碍方面的性能在21%至100%之间。人工智能技术在诊断精神健康障碍方面前景广阔。所报告的性能指标描绘了人工智能在该领域光明未来的生动图景。该领域的医疗保健专业人员应谨慎且有意识地开始探索基于人工智能的工具在日常工作中的应用机会。看到更多关于人工智能模型诊断其他常见精神障碍（如抑郁症和焦虑症）性能的荟萃分析和进一步的系统评价也将令人鼓舞。
