Psychiatric and Mental Health Nurses' Experiences and Perceptions of Artificial Intelligence in Clinical Practice: A Qualitative Study
Year: 2026
Type: article
Abstract: INTRODUCTION: Artificial intelligence is increasingly entering healthcare, yet its role in psychiatric and mental health nursing remains underexplored, particularly from nurses' experiential perspectives. AIM: This study aimed to explore psychiatric and mental health nurses' experiences of using generative AI tools in clinical practice, along with their broader perceptions and expectations of artificial intelligence. METHOD: A qualitative descriptive design was used. Semi-structured interviews were conducted with 23 psychiatric and mental health nurses recruited through purposive sampling. Data were analysed using Braun and Clarke's thematic analysis. RESULTS: Four themes emerged: trust-distrust; ease and speed of accessing medical content; limitations in meeting the individualized and relational demands of psychiatric care; and expectations from artificial intelligence. Nurses used artificial intelligence primarily for pharmacological and clinical information access, while adopting cautious, verification-based approaches due to accuracy concerns. DISCUSSION: Participants perceived limitations in AI's ability to address the individualized and relational dimensions of psychiatric care, while difficulties in prompt formulation emerged as a separate practical challenge. LIMITATIONS: Findings are context-specific and based on self-reported data. Participants shared similar educational backgrounds, which may have limited perspective diversity. IMPLICATIONS: Targeted training is needed to develop nurses' capacity to engage critically and effectively with artificial intelligence in clinical settings. RECOMMENDATIONS: Future integration of artificial intelligence into psychiatric nursing should be supported by structured education and system-level implementation strategies.
DOI: 10.1111/jpm.70216
Institutions: Koç University, Beykent University, Istanbul Arel University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W7225384008