■在线精神保健由于其有效性而获得了极大的关注,可访问性,以及心理健康症状管理的可扩展性。尽管与传统的面对面格式相比有这些优势,包括更高的可用性和可访问性,低治疗依从性和高辍学率的问题仍然存在.人工智能(AI)技术可以帮助解决这些问题,通过强大的预测模型,语言分析,与用户进行智能对话,然而,对这些应用的研究仍未充分开发。以下混合方法综述旨在通过综合人工智能在在线精神医疗中应用的现有证据来补充这一差距。
■我们搜索了以下数据库:MEDLINE,CINAHL,PsycINFO,EMBASE,还有Cochrane.本综述包括同行评审的随机对照试验,观察性研究,非随机实验研究,以及使用PRISMA指南选择的案例研究。提取并分析了有关干预前后结果和AI应用的数据。包括荟萃分析和网络荟萃分析的混合方法方法用于分析干预前后的结果,包括主要影响,抑郁症,焦虑,和研究辍学。我们应用了Cochrane偏差风险工具和建议分级评估,开发和评估(等级)以评估证据的质量。
■29项研究揭示了各种AI应用,包括分诊,提供心理治疗,治疗监测,治疗参与支持,识别有效的治疗特征,和治疗反应的预测,辍学,和坚持。人工智能提供的自我指导干预在管理心理健康症状方面表现出中等到较大的效果,辍学率与非人工智能干预相当。数据质量低到非常低。
■该评论支持使用AI增强治疗反应,坚持,和改善在线精神保健。然而,鉴于现有证据的质量不高,这项研究强调了在这一新兴领域需要更多稳健和高性能的研究.
■https://www.crd.约克。AC.uk/prospro/display_record.php?RecordID=443575,标识符CRD42023443575。
UNASSIGNED: Online mental healthcare has gained significant attention due to its effectiveness, accessibility, and scalability in the management of mental health symptoms. Despite these advantages over traditional in-person formats, including higher availability and accessibility, issues with low treatment adherence and high dropout rates persist. Artificial intelligence (AI) technologies could help address these issues, through powerful predictive models, language analysis, and intelligent dialogue with users, however the study of these applications remains underexplored. The following mixed methods
review aimed to supplement this gap by synthesizing the available evidence on the applications of AI in online mental healthcare.
UNASSIGNED: We searched the following databases: MEDLINE, CINAHL, PsycINFO, EMBASE, and Cochrane. This
review included peer-reviewed randomized controlled trials, observational studies, non-randomized experimental studies, and case studies that were selected using the PRISMA guidelines. Data regarding pre and post-intervention outcomes and AI applications were extracted and analyzed. A mixed-methods approach encompassing meta-analysis and network meta-analysis was used to analyze pre and post-intervention outcomes, including main effects, depression, anxiety, and study dropouts. We applied the Cochrane risk of bias tool and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) to assess the quality of the evidence.
UNASSIGNED: Twenty-nine studies were included revealing a variety of AI applications including triage, psychotherapy delivery, treatment monitoring, therapy engagement support, identification of effective therapy features, and prediction of treatment response, dropout, and adherence. AI-delivered self-guided interventions demonstrated medium to large effects on managing mental health symptoms, with dropout rates comparable to non-AI interventions. The quality of the data was low to very low.
UNASSIGNED: The
review supported the use of AI in enhancing treatment response, adherence, and improvements in online mental healthcare. Nevertheless, given the low quality of the available evidence, this study highlighted the need for additional robust and high-powered studies in this emerging field.
UNASSIGNED: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=443575, identifier CRD42023443575.