Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being.
Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being.
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DOI：
10.1038/s41746-023-00979-5
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发表时间：
2023-12-19
影响因子：
15.2
通讯作者：
中科院分区：
文献类型：
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作者：
会话式人工智能（AI），特别是基于AI的会话代理（ca），在精神卫生保健领域越来越受到关注。尽管它们的使用越来越多，但缺乏对其对心理健康和福祉的影响的全面评估。本系统综述和荟萃分析旨在通过综合基于人工智能的ca在改善心理健康方面的有效性以及影响其有效性和用户体验的因素的证据来填补这一空白。检索了12个数据库，检索了2023年5月26日之前发表的基于ai的CAs对精神疾病和心理健康影响的实验研究。7834项记录中，35项符合条件的研究被纳入系统评价，其中15项随机对照试验被纳入meta分析。荟萃分析显示，基于人工智能的ca可显著减轻抑郁（Hedge’s g 0.64 [95% CI 0.17-1.12]）和痛苦（Hedge’s g 0.7 [95% CI 0.18-1.22]）的症状。这些影响在多模式、基于生成式人工智能、与移动/即时通讯应用程序集成、针对临床/亚临床和老年人群的ca中更为明显。然而，基于ca的干预并未显示整体心理健康的显著改善（Hedge’s g = 0.32 [95% CI = -0.13 ~ 0.78]）。基于人工智能的ca的用户体验在很大程度上取决于人类与人工智能的治疗关系、内容参与和有效沟通的质量。这些发现强调了基于人工智能的ca在解决心理健康问题方面的潜力。未来的研究应该调查其有效性的潜在机制，评估各种心理健康结果的长期影响，并评估大语言模型（LLMs）在心理健康保健中的安全整合。
Conversational artificial intelligence (AI), particularly AI-based conversational agents (CAs), is gaining traction in mental health care. Despite their growing usage, there is a scarcity of comprehensive evaluations of their impact on mental health and well-being. This systematic review and meta-analysis aims to fill this gap by synthesizing evidence on the effectiveness of AI-based CAs in improving mental health and factors influencing their effectiveness and user experience. Twelve databases were searched for experimental studies of AI-based CAs’ effects on mental illnesses and psychological well-being published before May 26, 2023. Out of 7834 records, 35 eligible studies were identified for systematic review, out of which 15 randomized controlled trials were included for meta-analysis. The meta-analysis revealed that AI-based CAs significantly reduce symptoms of depression (Hedge’s g 0.64 [95% CI 0.17–1.12]) and distress (Hedge’s g 0.7 [95% CI 0.18–1.22]). These effects were more pronounced in CAs that are multimodal, generative AI-based, integrated with mobile/instant messaging apps, and targeting clinical/subclinical and elderly populations. However, CA-based interventions showed no significant improvement in overall psychological well-being (Hedge’s g 0.32 [95% CI –0.13 to 0.78]). User experience with AI-based CAs was largely shaped by the quality of human-AI therapeutic relationships, content engagement, and effective communication. These findings underscore the potential of AI-based CAs in addressing mental health issues. Future research should investigate the underlying mechanisms of their effectiveness, assess long-term effects across various mental health outcomes, and evaluate the safe integration of large language models (LLMs) in mental health care.
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影响因子：
7.4
作者：
He, Yuhao;Yang, Li;Zhu, Xiaokun;Wu, Bin;Zhang, Shuo;Qian, Chunlian;Tian, Tian
通讯作者：
Tian, Tian
影响因子：
--
作者：
Daley K;Hungerbuehler I;Cavanagh K;Claro HG;Swinton PA;Kapps M
通讯作者：
Kapps M
DOI：
10.3390/healthcare10010133
发表时间：
2022-01-10
期刊：
Healthcare (Basel, Switzerland)
影响因子：
--
作者：
Goga N;Boiangiu CA;Vasilateanu A;Popovici AF;Drăgoi MV;Popovici R;Gancea IO;Pîrlog MC;Popa RC;Hadăr A
通讯作者：
Hadăr A
影响因子：
7.4
作者：
He, Yuhao;Yang, Li;Qian, Chunlian;Li, Tong;Su, Zhengyuan;Zhang, Qiang;Hou, Xiangqing
通讯作者：
Hou, Xiangqing
影响因子：
9.9
作者：
Drouin, Michelle;Sprecher, Susan;Perkins, Taylor
通讯作者：
Perkins, Taylor