PDF(Pubmed)
Abstract：
BACKGROUND: Mental disorders have ranked among the top 10 prevalent causes of burden on a global scale. Generative artificial intelligence (GAI) has emerged as a promising and innovative technological advancement that has significant potential in the field of mental health care. Nevertheless, there is a scarcity of research dedicated to examining and understanding the application landscape of GAI within this domain.
OBJECTIVE: This review aims to inform the current state of GAI knowledge and identify its key uses in the mental health domain by consolidating relevant literature.
METHODS: Records were searched within 8 reputable sources including Web of Science, PubMed, IEEE Xplore, medRxiv, bioRxiv, Google Scholar, CNKI and Wanfang databases between 2013 and 2023. Our focus was on original, empirical research with either English or Chinese publications that use GAI technologies to benefit mental health. For an exhaustive search, we also checked the studies cited by relevant literature. Two reviewers were responsible for the data selection process, and all the extracted data were synthesized and summarized for brief and in-depth analyses depending on the GAI approaches used (traditional retrieval and rule-based techniques vs advanced GAI techniques).
RESULTS: In this review of 144 articles, 44 (30.6%) met the inclusion criteria for detailed analysis. Six key uses of advanced GAI emerged: mental disorder detection, counseling support, therapeutic application, clinical training, clinical decision-making support, and goal-driven optimization. Advanced GAI systems have been mainly focused on therapeutic applications (n=19, 43%) and counseling support (n=13, 30%), with clinical training being the least common. Most studies (n=28, 64%) focused broadly on mental health, while specific conditions such as anxiety (n=1, 2%), bipolar disorder (n=2, 5%), eating disorders (n=1, 2%), posttraumatic stress disorder (n=2, 5%), and schizophrenia (n=1, 2%) received limited attention. Despite prevalent use, the efficacy of ChatGPT in the detection of mental disorders remains insufficient. In addition, 100 articles on traditional GAI approaches were found, indicating diverse areas where advanced GAI could enhance mental health care.
CONCLUSIONS: This study provides a comprehensive overview of the use of GAI in mental health care, which serves as a valuable guide for future research, practical applications, and policy development in this domain. While GAI demonstrates promise in augmenting mental health care services, its inherent limitations emphasize its role as a supplementary tool rather than a replacement for trained mental health providers. A conscientious and ethical integration of GAI techniques is necessary, ensuring a balanced approach that maximizes benefits while mitigating potential challenges in mental health care practices.
OBJECTIVE: This review aims to inform the current state of GAI knowledge and identify its key uses in the mental health domain by consolidating relevant literature.
METHODS: Records were searched within 8 reputable sources including Web of Science, PubMed, IEEE Xplore, medRxiv, bioRxiv, Google Scholar, CNKI and Wanfang databases between 2013 and 2023. Our focus was on original, empirical research with either English or Chinese publications that use GAI technologies to benefit mental health. For an exhaustive search, we also checked the studies cited by relevant literature. Two reviewers were responsible for the data selection process, and all the extracted data were synthesized and summarized for brief and in-depth analyses depending on the GAI approaches used (traditional retrieval and rule-based techniques vs advanced GAI techniques).
RESULTS: In this review of 144 articles, 44 (30.6%) met the inclusion criteria for detailed analysis. Six key uses of advanced GAI emerged: mental disorder detection, counseling support, therapeutic application, clinical training, clinical decision-making support, and goal-driven optimization. Advanced GAI systems have been mainly focused on therapeutic applications (n=19, 43%) and counseling support (n=13, 30%), with clinical training being the least common. Most studies (n=28, 64%) focused broadly on mental health, while specific conditions such as anxiety (n=1, 2%), bipolar disorder (n=2, 5%), eating disorders (n=1, 2%), posttraumatic stress disorder (n=2, 5%), and schizophrenia (n=1, 2%) received limited attention. Despite prevalent use, the efficacy of ChatGPT in the detection of mental disorders remains insufficient. In addition, 100 articles on traditional GAI approaches were found, indicating diverse areas where advanced GAI could enhance mental health care.
CONCLUSIONS: This study provides a comprehensive overview of the use of GAI in mental health care, which serves as a valuable guide for future research, practical applications, and policy development in this domain. While GAI demonstrates promise in augmenting mental health care services, its inherent limitations emphasize its role as a supplementary tool rather than a replacement for trained mental health providers. A conscientious and ethical integration of GAI techniques is necessary, ensuring a balanced approach that maximizes benefits while mitigating potential challenges in mental health care practices.
摘要：
背景：在全球范围内，精神障碍已被列为造成负担的十大常见原因之一。生成人工智能（GAI）已经成为一种有前途和创新的技术进步，在精神卫生保健领域具有巨大的潜力。然而，缺乏专门研究和了解GAI在该领域内的应用前景的研究。
目的：本综述旨在通过整合相关文献，了解GAI知识的现状，并确定其在心理健康领域的关键用途。
方法：在包括WebofScience在内的8个知名来源中搜索了记录，PubMed,IEEEXplore,medRxiv,bioRxiv,谷歌学者，2013年至2023年的CNKI和万方数据库。我们的重点是原创，使用GAI技术有益于心理健康的英文或中文出版物进行实证研究。为了进行详尽的搜索，我们还检查了相关文献引用的研究。两名审查人员负责数据选择过程，根据所使用的GAI方法（传统检索和基于规则的技术与先进的GAI技术），对所有提取的数据进行了综合和总结，以进行简短深入的分析。
结果：在对144篇文章的评论中，44(30.6%)符合详细分析的纳入标准。出现了高级GAI的六个关键用途：精神障碍检测，咨询支持,治疗应用，临床培训,临床决策支持，和目标驱动的优化。先进的GAI系统主要集中在治疗应用（n=19，43％）和咨询支持（n=13，30％），临床培训是最不常见的。大多数研究（n=28，64％）广泛关注心理健康，而特定条件如焦虑(n=1，2%)，双相情感障碍（n=2，5%），饮食失调（n=1，2％），创伤后应激障碍（n=2，5%），精神分裂症（n=1，2％）受到的关注有限。尽管普遍使用，ChatGPT在检测精神障碍方面的功效仍然不足.此外,发现了100篇关于传统GAI方法的文章，表明先进的GAI可以增强精神卫生保健的不同领域。
结论：本研究全面概述了GAI在精神保健中的应用，作为未来研究的宝贵指南，实际应用,以及这一领域的政策制定。虽然GAI在加强精神卫生保健服务方面表现出了希望，其固有的局限性强调了其作为补充工具的作用，而不是替代训练有素的心理健康提供者。有必要对GAI技术进行认真和道德的整合，确保采取平衡的方法，最大限度地提高利益，同时减轻精神卫生保健实践中的潜在挑战。
目的：本综述旨在通过整合相关文献，了解GAI知识的现状，并确定其在心理健康领域的关键用途。
方法：在包括WebofScience在内的8个知名来源中搜索了记录，PubMed,IEEEXplore,medRxiv,bioRxiv,谷歌学者，2013年至2023年的CNKI和万方数据库。我们的重点是原创，使用GAI技术有益于心理健康的英文或中文出版物进行实证研究。为了进行详尽的搜索，我们还检查了相关文献引用的研究。两名审查人员负责数据选择过程，根据所使用的GAI方法（传统检索和基于规则的技术与先进的GAI技术），对所有提取的数据进行了综合和总结，以进行简短深入的分析。
结果：在对144篇文章的评论中，44(30.6%)符合详细分析的纳入标准。出现了高级GAI的六个关键用途：精神障碍检测，咨询支持,治疗应用，临床培训,临床决策支持，和目标驱动的优化。先进的GAI系统主要集中在治疗应用（n=19，43％）和咨询支持（n=13，30％），临床培训是最不常见的。大多数研究（n=28，64％）广泛关注心理健康，而特定条件如焦虑(n=1，2%)，双相情感障碍（n=2，5%），饮食失调（n=1，2％），创伤后应激障碍（n=2，5%），精神分裂症（n=1，2％）受到的关注有限。尽管普遍使用，ChatGPT在检测精神障碍方面的功效仍然不足.此外,发现了100篇关于传统GAI方法的文章，表明先进的GAI可以增强精神卫生保健的不同领域。
结论：本研究全面概述了GAI在精神保健中的应用，作为未来研究的宝贵指南，实际应用,以及这一领域的政策制定。虽然GAI在加强精神卫生保健服务方面表现出了希望，其固有的局限性强调了其作为补充工具的作用，而不是替代训练有素的心理健康提供者。有必要对GAI技术进行认真和道德的整合，确保采取平衡的方法，最大限度地提高利益，同时减轻精神卫生保健实践中的潜在挑战。
