PERSPECTIVE article
Front. Psychiatry
Sec. Digital Mental Health
This article is part of the Research TopicAdvances in Generative Artificial Intelligence for Mental HealthView all 6 articles
The Augmented Clinician as a Framework for Human-AI Collaboration in Mental Healthcare
Provisionally accepted
- 1Department of Psychiatry, Wenzhou Seventh Peoples Hospital, Wenzhou, China
- 2Wenzhou Center for Disease Control and Prevention, Wenzhou, China
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The global mental health system faces an unprecedented crisis of access, with demand for care far outstripping the supply of trained professionals. Artificial Intelligence (AI) has emerged with immense promise to bridge this gap through scalable and accessible solutions. However, its rapid and often unregulated deployment introduces significant ethical perils, including the dehumanization of care, the perpetuation of societal biases, and the risk of clinical harm. This perspective argues against the pursuit of autonomous AI therapists and instead advocates for the Augmented Clinician model. This framework positions AI as a sophisticated and transparent supportive tool that enhances, rather than replaces, human clinicians. By delegating data-intensive and administrative tasks to AI, clinicians can dedicate more time to the irreplaceable human elements of therapy such as empathy, nuanced judgment, and fostering the therapeutic alliance. We propose that this collaborative human-AI synergy is the most effective and ethically sound path to harness technology's power while ensuring mental healthcare remains fundamentally human-centered.
Keywords: artificial intelligence, Augmented Clinician, Ethics, human-AI collaboration, Mental Health
Received: 21 Oct 2025; Accepted: 29 Jan 2026.
Copyright: © 2026 Ruan, Hu and ShangGuan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Qiannan Ruan
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