A Scoping Review of AI-Driven Digital Interventions in Mental Health Care: Mapping Applications Across Screening, Support, Monitoring, Prevention, and Clinical Education.

IF 2.4 4区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Yang Ni, Fanli Jia
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引用次数: 0

Abstract

Background/objectives: Artificial intelligence (AI)-enabled digital interventions are increasingly used to expand access to mental health care. This PRISMA-ScR scoping review maps how AI technologies support mental health care across five phases: pre-treatment (screening), treatment (therapeutic support), post-treatment (monitoring), clinical education, and population-level prevention.

Methods: We synthesized findings from 36 empirical studies published through January 2024 that implemented AI-driven digital tools, including large language models (LLMs), machine learning (ML) models, and conversational agents. Use cases include referral triage, remote patient monitoring, empathic communication enhancement, and AI-assisted psychotherapy delivered via chatbots and voice agents.

Results: Across the 36 included studies, the most common AI modalities included chatbots, natural language processing tools, machine learning and deep learning models, and large language model-based agents. These technologies were predominantly used for support, monitoring, and self-management purposes rather than as standalone treatments. Reported benefits included reduced wait times, increased engagement, and improved symptom tracking. However, recurring challenges such as algorithmic bias, data privacy risks, and workflow integration barriers highlight the need for ethical design and human oversight.

Conclusion: By introducing a four-pillar framework, this review offers a comprehensive overview of current applications and future directions in AI-augmented mental health care. It aims to guide researchers, clinicians, and policymakers in developing safe, effective, and equitable digital mental health interventions.

精神卫生保健中人工智能驱动的数字干预的范围审查:跨筛查、支持、监测、预防和临床教育的映射应用。
背景/目标:支持人工智能(AI)的数字干预措施越来越多地用于扩大获得精神卫生保健的机会。这项PRISMA-ScR范围审查描绘了人工智能技术如何在五个阶段支持精神卫生保健:治疗前(筛查)、治疗(治疗支持)、治疗后(监测)、临床教育和人群层面的预防。方法:我们综合了截至2024年1月发表的36项实证研究的结果,这些研究实施了人工智能驱动的数字工具,包括大型语言模型(llm)、机器学习(ML)模型和会话代理。用例包括转诊分诊、远程患者监护、共情沟通增强以及通过聊天机器人和语音代理提供的人工智能辅助心理治疗。结果:在36项纳入的研究中,最常见的人工智能模式包括聊天机器人、自然语言处理工具、机器学习和深度学习模型,以及基于大型语言模型的代理。这些技术主要用于支持、监控和自我管理目的,而不是作为独立的治疗方法。报告的好处包括减少了等待时间,增加了参与度,改善了症状跟踪。然而,诸如算法偏见、数据隐私风险和工作流集成障碍等反复出现的挑战突出了道德设计和人为监督的必要性。结论:通过引入四支柱框架,本文综述了人工智能增强精神卫生保健的当前应用和未来方向。它旨在指导研究人员、临床医生和政策制定者制定安全、有效和公平的数字心理健康干预措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Healthcare
Healthcare Medicine-Health Policy
CiteScore
3.50
自引率
7.10%
发文量
0
审稿时长
47 days
期刊介绍: Healthcare (ISSN 2227-9032) is an international, peer-reviewed, open access journal (free for readers), which publishes original theoretical and empirical work in the interdisciplinary area of all aspects of medicine and health care research. Healthcare publishes Original Research Articles, Reviews, Case Reports, Research Notes and Short Communications. We encourage researchers to publish their experimental and theoretical results in as much detail as possible. For theoretical papers, full details of proofs must be provided so that the results can be checked; for experimental papers, full experimental details must be provided so that the results can be reproduced. Additionally, electronic files or software regarding the full details of the calculations, experimental procedure, etc., can be deposited along with the publication as “Supplementary Material”.
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