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Artificial intelligence in psychiatric care and education: a qualitative study of factors influencing adoption in Singapore.

Aug 2026 · International Journal of Medical Education · Vol 17, pp. 121-130 · 0 citations
Medicine

TL;DR

Effective AI adoption in psychiatric care and education can be facilitated by clearly delineating tasks, embedding digital literacy into training, communicating robust governance structures, and introducing peer-led communities of practice.

Abstract

Objectives This study aimed to understand how AI's perceived role interacts with emerging barriers and facilitators to identify factors influencing its adoption across clinical and educational professions in Singapore. Methods This study followed a qualitative approach guided by a medical-pedagogical theoretical framework. Semi-structured interviews were conducted between May and July 2025 and followed an interview guide based on the medical-pedagogical framework. Twenty-four people participated, including eight nurses, six psychiatrists, and 10 allied health professionals. All were clinicians and educators. Data were analysed using thematic analysis, with attention to emergent patterns across patient care and educational contexts. Results Participants recognised significant potential for AI in patient care and healthcare professions education, particularly for information access, retrieval, clinical documentation, AI-augmented training methods such as virtual patients and educational content creation. Barriers included fears of professional skill degradation, role confusion, lack of familiarity with capabilities and the need for personal evidence of benefit. Enablers encompassed integrated, context-specific training, clear governance frameworks, and peer networks facilitating experiential learning and responsible use. Participants emphasised maintaining human connection and reflective practice as essential to psychiatric care and education. Conclusions Effective AI adoption in psychiatric care and education can be facilitated by clearly delineating tasks, embedding digital literacy into training, communicating robust governance structures, and introducing peer-led communities of practice. Relevant stakeholders should be engaged to align AI deployment with real clinical workflows, optimising both patient care and educational outcomes.

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