“我相信人工智能将在问题发生之前识别问题”:探索年轻人对人工智能在精神卫生保健中的看法的定性研究。

IF 5.8 2区 医学 Q1 PSYCHIATRY
Jmir Mental Health Pub Date : 2025-08-25 DOI:10.2196/76973
Lena Petersson, Mikael G Ahlborg, Katrin Häggström Westberg
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引用次数: 0

摘要

背景:在全球范围内,有精神健康问题的年轻人难以获得适当和及时的护理,这可能导致未来预后较差。建议利用人工智能(AI)提高诊断、监测、获取、高级决策和数字咨询方面的能力,从而提高精神卫生保健的质量。在精神卫生保健领域,人工智能解决方案的设计和应用应该阐明患者对人工智能的看法。目的:从有常见心理健康问题求助经历的年轻人的角度,探讨人工智能在心理卫生保健中的认知。方法:对25名年龄在18岁至30岁之间的年轻人进行访谈研究,采用定性归纳设计和内容分析,探讨如何将基于人工智能的技术应用于精神卫生保健。结果:从分析中得出了三个类别,代表了参与者对如何使用基于人工智能的技术来治疗精神健康问题的看法。第一类包括将基于人工智能的技术视为数字伴侣,在困难时期支持个人,提醒和建议自我保健活动,建议信息来源,以及通常接受行为或情绪的变化。第二类围绕人工智能实现更有效的护理和作为工具的功能,无论是对患者还是医疗保健专业人员(HCPs)。年轻人对人工智能改善分诊、筛查、识别和诊断充满信心。第三类是对人工智能的风险和怀疑,认为人工智能是有局限性的人类开发的产品。年轻人表达了对安全和诚信的担忧,并担心人工智能是自主的,没有人类的同理心,但具有很强的预测能力。结论:年轻人认识到人工智能作为个性化支持的潜力,以及它作为精神卫生保健咨询之间的数字指南和伴侣的功能。据信,人工智能将在寻求帮助的过程中发挥支持作用,确保他们避免“中间缺失”的服务差距。他们还表示,人工智能将通过监测、诊断准确性和减少医护人员的工作量来提高医疗保健的效率,同时减少年轻人反复讲述他们的故事的需要。年轻人对在医疗保健中使用人工智能表达了矛盾的态度,并提出了数据完整性和偏见的风险。他们认为人工智能比hcp更理性和客观,但不想放弃与人类的个人互动。基于这项研究的结果和年轻人对人工智能监测能力的看法,未来的研究应该界定卫生保健系统的信息收集责任与个人自我保健责任之间的界限。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

"I Believe That AI Will Recognize the Problem Before It Happens": Qualitative Study Exploring Young Adults' Perceptions of AI in Mental Health Care.

"I Believe That AI Will Recognize the Problem Before It Happens": Qualitative Study Exploring Young Adults' Perceptions of AI in Mental Health Care.

Background: Globally, young adults with mental health problems struggle to access appropriate and timely care, which may lead to a poorer future prognosis. Artificial intelligence (AI) is suggested to improve the quality of mental health care through increased capacities in diagnostics, monitoring, access, advanced decision-making, and digital consultations. Within mental health care, the design and application of AI solutions should elucidate the patient perspective on AI.

Objective: The aim was to explore the perceptions of AI in mental health care from the viewpoint of young adults with experience of seeking help for common mental health problems.

Methods: This was an interview study with 25 young adults aged between 18 and 30 years that applied a qualitative inductive design, with content analysis, to explore how AI-based technology can be used in mental health care.

Results: Three categories were derived from the analysis, representing the participants' perceptions of how AI-based technology can be used in care for mental health problems. The first category entailed perceptions of AI-based technology as a digital companion, supporting individuals at difficult times, reminding and suggesting self-care activities, suggesting sources of information, and generally being receptive to changes in behavior or mood. The second category revolved around AI enabling more effective care and functioning as a tool, both for the patient and health care professionals (HCPs). Young adults expressed confidence in AI to improve triage, screening, identification, and diagnosis. The third category concerned risks and skepticism toward AI as a product developed by humans with limitations. Young adults voiced concerns about security and integrity, and about AI being autonomous, incapable of human empathy but with strong predictive capabilities.

Conclusions: Young adults recognize the potential of AI to serve as personalized support and its function as a digital guide and companion between mental health care consultations. It was believed that AI would function as a support in navigating the help-seeking process, ensuring that they avoid the "missing middle" service gap. They also voiced that AI will improve efficiency in health care, through monitoring, diagnostic accuracy, and reduction of the workload of HCPs, while simultaneously reducing the need for young adults to repeatedly tell their stories. Young adults express an ambivalence toward the use of AI in health care and voice risks of data integrity and bias. They consider AI to be more rational and objective than HCPs but do not want to forsake personal interaction with humans. Based on the results of this study and young adults' perceptions of the monitoring capabilities of AI, future studies should define the boundaries regarding information collection responsibilities of the health care system versus the individuals' responsibility for self-care.

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来源期刊
Jmir Mental Health
Jmir Mental Health Medicine-Psychiatry and Mental Health
CiteScore
10.80
自引率
3.80%
发文量
104
审稿时长
16 weeks
期刊介绍: JMIR Mental Health (JMH, ISSN 2368-7959) is a PubMed-indexed, peer-reviewed sister journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR Mental Health focusses on digital health and Internet interventions, technologies and electronic innovations (software and hardware) for mental health, addictions, online counselling and behaviour change. This includes formative evaluation and system descriptions, theoretical papers, review papers, viewpoint/vision papers, and rigorous evaluations.
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