Clinician perceptions of artificial intelligence in care: A mixed methods study (Preprint)

Abstract

<sec> <title>BACKGROUND</title> AI is increasingly being explored as a tool to enhance efficiency, access, and diagnostic accuracy in mental health care. However, the perspectives on the use of AI from clinicians, who are central to the delivery and oversight of care, remain underexamined. </sec> <sec> <title>OBJECTIVE</title> This study aimed to explore clinicians' perceptions of AI in clinical care, including perceived benefits, risks, barriers to implementation, and training needs. </sec> <sec> <title>METHODS</title> A cross-sectional mixed-methods survey was distributed from August to November 2024 to mental health professionals in the US. Quantitative data were analyzed using descriptive statistics, while open-ended responses were analyzed thematically to identify key insights. </sec> <sec> <title>RESULTS</title> Respondents (n=62) were mostly not currently using AI in practice. The most frequently endorsed benefits included reductions in clinical workload, improved efficiency, and enhanced data analysis. However, a majority expressed discomfort using AI in patient care, and concerns were raised about inaccurate outputs, algorithmic bias, privacy, and weakened therapeutic rapport. Barriers to adoption included clinician resistance, lack of validation, and challenges with technical integration. Most respondents believed that specialized training in AI ethics and applications is important for clinicians. Qualitative findings reinforced concerns about dehumanization, cultural insensitivity, ethical accountability, and insufficient technological literacy. </sec> <sec> <title>CONCLUSIONS</title> Mental health professionals view AI as a potentially useful adjunct, but not a replacement, in care. Ethical concerns, limited trust, and a strong emphasis on the human dimensions of therapy suggest that implementation must proceed with caution. Clinician-informed strategies, ethical frameworks, and targeted training are essential to support the responsible and effective integration of AI into mental health practice. </sec>

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