Xuechang Xian, Angela Chang, Yu-Tao Xiang, Matthew Tingchi Liu
{"title":"生成式人工智能在心理健康护理中的辩论与困境:范围审查。","authors":"Xuechang Xian, Angela Chang, Yu-Tao Xiang, Matthew Tingchi Liu","doi":"10.2196/53672","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":51757,"journal":{"name":"Interactive Journal of Medical Research","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11347908/pdf/","citationCount":"0","resultStr":"{\"title\":\"Debate and Dilemmas Regarding Generative AI in Mental Health Care: Scoping Review.\",\"authors\":\"Xuechang Xian, Angela Chang, Yu-Tao Xiang, Matthew Tingchi Liu\",\"doi\":\"10.2196/53672\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":51757,\"journal\":{\"name\":\"Interactive Journal of Medical Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11347908/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Interactive Journal of Medical Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2196/53672\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Interactive Journal of Medical Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2196/53672","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
摘要
背景:精神障碍已跻身全球十大普遍负担原因之列。生成式人工智能(GAI)已成为一项前景广阔的创新技术进步,在精神卫生保健领域具有巨大潜力。然而,专门研究和了解 GAI 在这一领域应用前景的研究却很少: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主要用途:在 2013 年至 2023 年期间,我们在 8 个知名来源中搜索了相关记录,包括 Web of Science、PubMed、IEEE Xplore、medRxiv、bioRxiv、Google Scholar、CNKI 和万方数据库。我们的重点是使用 GAI 技术造福心理健康的原创性实证研究,包括英文或中文出版物。为了进行详尽的搜索,我们还检查了相关文献引用的研究。两名审稿人负责数据筛选过程,并根据所使用的 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综合和总结,以进行简要和深入分析:在这篇包含 144 篇文章的综述中,有 44 篇(30.6%)符合详细分析的纳入标准。高级 GAI 的六个主要用途是: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优化。高级 GAI 系统主要集中于治疗应用(19 项,占 43%)和咨询支持(13 项,占 30%),临床培训是最少见的。大多数研究(n=28,64%)广泛关注心理健康,而焦虑症(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%)和精神分裂症(n=1,2%)等特定病症受到的关注有限。尽管 ChatGPT 的使用非常普遍,但其在检测精神障碍方面的功效仍然不足。此外,还发现了 100 篇关于传统 GAI 方法的文章,这表明先进的 GAI 可以在不同领域提高精神卫生保健水平:本研究全面概述了 GAI 在精神健康护理中的应用,为该领域的未来研究、实际应用和政策制定提供了宝贵的指导。虽然 GAI 在增强心理健康护理服务方面大有可为,但其固有的局限性强调了它作为辅助工具的作用,而不是取代训练有素的心理健康服务提供者。有必要对 GAI 技术进行有意识的、符合道德规范的整合,确保采用一种平衡的方法,在最大限度地提高效益的同时,减轻心理健康护理实践中的潜在挑战。
Debate and Dilemmas Regarding Generative AI in Mental Health Care: Scoping Review.
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.