Between Care and Code: Clinicians’ Perspectives as an Ethical Lens for AI in Mental Health – Insights from an Interview-Based Case Study

Abstract

Artificial intelligence (AI) is rapidly transforming the field of mental health care, offering new opportunities for diagnosis, treatment, documentation, and access to services. While AI tools promise efficiency, scalability, and innovation, they also raise concerns related to empathy, ethics, bias, inclusivity, and professional identity. This paper explores clinicians’ responses to the integration of AI in mental health, highlighting the tensions that emerge between efficiency and empathy, automation and therapeutic presence, and access and equity. Drawing on thematic analysis of recent empirical and conceptual literature, the discussion identifies three central themes: (1) clinicians’ ambivalence toward AI reflects both anxiety about displacement and optimism about support; (2) the ethical challenges of AI—including risks of bias, loss of trust, and inequitable access—require careful design and governance; and (3) AI should supplement, rather than replace, human care, preserving therapeutic relationships as the cornerstone of mental health practice. The paper argues that ambivalence itself is a valuable ethical resource: it reflects clinicians’ constructive visions for responsible innovation. By integrating these perspectives, the review underscores the need for balanced policies, inclusive design, and ongoing dialogue among clinicians, developers, and policymakers. A conceptual table is provided to illustrate the interplay of efficiency, empathy, and equity in shaping clinicians’ experiences and expectations. Overall, the paper contributes to critical debates on the future of AI in mental health, emphasizing that sustainable adoption depends not only on technological advancement but also on protecting the human dimensions of care.

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