Clinicians’ Attitudes and Perceptions on the Adoption of AI in Mental Health Care: Scoping Review

Background: AI is increasingly being integrated into health care workflows, with growing interest in AI systems for documentation, screening, triage, monitoring, and decision support. Mental health care is particularly complex for AI implementation as clinical care largely depends on therapeutic relationships, contextual factors, empathic communication, and interpretation of subtle nonverbal cues. Although AI may offer opportunities to improve efficiency and access to care, its adoption is likely to depend on clinicians' trust, ethical acceptability, safety, confidentiality, and clarity around professional responsibility. Objective: This scoping review aims to synthesize current evidence on mental health clinicians' attitudes, perceptions, and beliefs regarding the use of AI tools within mental health care, including the perceived benefits, risks, acceptable use cases, and conditions considered necessary for implementation. Methods: A scoping review was conducted in accordance with Joanna Briggs Institute (JBI) guidance and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Six databases (CINAHL, Embase, PsycINFO, PubMed, Scopus, Web of Science) were searched on May 5, 2026, for studies published from 2020 onwards. Studies were eligible if they examined clinicians' attitudes, perceptions, or beliefs regarding AI in mental health care. Studies were screened by 2 reviewers, first by title and abstract, and then by full text. Data extraction included publication year, country, methodology, participant occupation, AI type, outcome domains, and relevant findings. Results: Database searches retrieved 12,356 records. Following screening, 35 records were included. Overall, clinicians demonstrated cautious optimism toward AI, particularly when positioned as supplementing instead of replacing clinical expertise. Perceived benefits centered on reducing administrative burden, supporting documentation, synthesizing large volumes of information, and improving access to care, particularly between sessions. However, clinicians also reported limited AI literacy and prior use, and were concerned about privacy, confidentiality, governance, data ownership, clinical safety, unsafe or inaccurate outputs, overreliance, unclear accountability, professional role boundaries, and impacts on therapeutic relationships. Clinicians emphasized the need for education, clear guidelines and governance, role clarity, co-designed systems, human oversight, and real-world, ongoing evaluation. Conclusions: This review is innovative in shifting focus from the technical performance of AI tools to the perspectives of the clinicians who will be expected to use, interpret, explain, and remain accountable for them in mental health care. Unlike previous reviews, this review provides a clinician-centered understanding of AI adoption, highlighting that acceptability depends not only on what AI can do but also on whether it can be integrated safely, ethically, and in ways that preserve professional judgment and therapeutic relationships. These findings suggest that implementation should begin with lower-risk (eg, administrative), clinician-facing applications, be supported by education and governance, and be evaluated in mental health settings before wider adoption. These insights provide practical direction for responsible, clinician-centered implementation across mental health services.