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Perception of AI Use in Youth Mental Health Services: Qualitative Study
Xiaoxu Ding1, Skye Barbic1,2
1Department of Occupational Science & Occupational Therapy, Faculty of Medicine, University of British Columbia, 317-2194 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada, 1 2369928222.
Background:
Artificial intelligence (AI) technology has made significant advancements in health care. A key application of using artificial intelligence for health (AIH) is the use of AI-powered chatbots; however, empirical evidence on their effectiveness and feasibility remains limited.
Objective:
This study explored interest group perceptions of integrating AIH in youth mental health services, focusing on its potential benefits, challenges, usefulness, and regulatory implications.
Methods:
This qualitative study used semistructured in-depth interviews with 23 mobile health stakeholders, including youth users, service providers, and nonclinical staff from an integrated youths' service network. We used an inductive approach and thematic analysis to identify and summarize common themes and subthemes.
Results:
Participants identified AIH's potential to support education, navigation, and administrative tasks in health care, as well as to create safe spaces and mitigate health resource burdens. However, they expressed concerns about the lack of human elements, such as empathy and clinical judgment. Key challenges included privacy issues, unknown risks from rapid technological advancements, and insufficient crisis management for sensitive mental health cases. Participants viewed AIH's ability to mimic human behavior as a critical quality standard and emphasized the need for a robust evaluation framework combining objective metrics with subjective insights.
Conclusions:
While AIH has the potential to improve health care access and experience, it cannot address all mental health challenges and may exacerbate existing issues. While AIH could complement less-complex services, it could not replace the therapeutic value of human interaction at this time. Co-design with end users is critical for successful AI integration. Robust evaluation frameworks and an iterative approach to build a learning health system are essential to refine AIH and ensure it aligns with real-world evolving needs.
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