OBJECTIVE: The primary aim was to use a demographically diverse sample to validate an AI model, previously trained on human-administered interviews, on novel bot-administered interviews, and to check for algorithmic biases related to age, sex, race, and ethnicity.
METHODS: Using the Aiberry app, adults recruited via social media (N = 393) completed a brief bot-administered interview and a depression self-report form. An AI model was used to predict form scores based on interview responses alone. For all meaningful discrepancies between model inference and form score, clinicians performed a masked review to determine which one they preferred.
RESULTS: There was strong concurrent validity between the model predictions and raw self-report scores (r = 0.73, MAE = 3.3). 90 % of AI predictions either agreed with self-report or with clinical expert opinion when AI contradicted self-report. There was no differential model performance across age, sex, race, or ethnicity.
CONCLUSIONS: Limitations include access restrictions (English-speaking ability and access to smartphone or computer with broadband internet) and potential self-selection of participants more favorably predisposed toward AI technology.
CONCLUSIONS: The Aiberry model made accurate predictions of depression severity based on remotely collected spoken responses to a bot-administered interview. This study shows promising results for the use of AI as a mental health screening tool on par with self-report measures.
目标:主要目的是使用人口统计学上不同的样本来验证AI模型,以前接受过人类管理的采访,在新颖的机器人管理的采访中,并检查与年龄相关的算法偏差,性别,种族,和种族。
方法:使用Aiberry应用程序,通过社交媒体招募的成年人(N=393)完成了简短的机器人管理访谈和抑郁症自我报告表。使用AI模型仅根据面试回答来预测表格分数。对于模型推断和形式评分之间的所有有意义的差异,临床医生进行了一项隐蔽审查,以确定他们更喜欢哪一项.
结果:模型预测和原始自我报告得分之间存在很强的并发有效性(r=0.73,MAE=3.3)。当AI与自我报告相矛盾时,90%的AI预测要么与自我报告一致,要么与临床专家意见一致。不同年龄的模型性能没有差异,性别,种族,或种族。
结论:限制包括访问限制(讲英语的能力以及使用宽带互联网的智能手机或计算机)以及对AI技术更有利的参与者的潜在自我选择。
结论:Aiberry模型根据远程收集的对机器人管理的访谈的口头反应,对抑郁症的严重程度做出了准确的预测。这项研究显示,在使用AI作为与自我报告措施相当的心理健康筛查工具方面,有希望的结果。