The role of generative artificial intelligence in psychiatric education– a scoping review
Open accessAbstract
BACKGROUND: The growing prevalence of mental health conditions, worsened by the COVID-19 pandemic, highlights the urgent need for enhanced psychiatric education. The distinctive nature of psychiatry- which is heavily centred on communication skills, interpersonal skills, and interviewing techniques- indicates a necessity for further research into the use of GenAI in psychiatric education. OBJECTIVE: Given GenAI has shown promising outcomes in medical education, this study aims to discuss the possible roles of GenAI in psychiatric education. METHODS: We conducted a scoping review to identify the role of GenAI in psychiatric education based on the educational framework of the Canadian Medical Education Directives for Specialists (CanMEDS). RESULTS: Of the 12,594 papers identified, five studies met the inclusion criteria, revealing key roles for GenAI in case-based learning, simulation, content synthesis, and assessments. Despite these promising applications, limitations such as content accuracy, biases, and concerns regarding security and privacy were highlighted. CONCLUSIONS: Despite these promising applications, limitations such as content accuracy, biases, and concerns regarding security and privacy have been highlighted. This study contributes to understanding how GenAI can enhance psychiatric education and suggests future research directions to refine its use in training medical students and primary care physicians. GenAI has significant potential to address the growing demand for mental health professionals, provided its limitations are carefully managed.
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Transformer-based LLMs demonstrate improved realism and adaptability in simulating a wide range of patient scenarios relevant to consultation practices and education.
“Transformer-based LLMs [19], trained on terabytes of data, demonstrate improved realism and adaptability in simulating a wide range of patient scenarios relevant to consultation practices [20] and education [2122].”
From Enhancing Psychiatry Training Using an Agentic AI Simulated Consultation Tool: Prospective Cohort Study (2026) · cites this paper as [20]
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- Authors
- Lee, Q. Y., Chen, M., Ong, C. W., & Ho, C. S. H.
- Affiliations
- Nanyang Technological University (SG)
- National University of Singapore (SG)
- Corresponding author
- Ho, Cyrus Su Hui
- ORCID
- Ho, Cyrus Su Hui
- Venue
- BMC Medical Education
- Published
- 2025-03-25
- Volume
- 25
- Issue
- 1
- Pages
- 438-438
- ISSN
- 1472-6920 (ISSN-L)
- PMC
- PMC11938615
- OpenAlex
- W4408823800
- Cited by
- 23
- References
- 30
- Citations by year
- 2026: 14 · 2025: 9
- FWCI
- 1.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Education (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- generative AI, psychiatric training, medical education, CanMEDS, case-based learning, simulation-based training, assessment, communication skills, algorithmic bias, data security and privacy, mental health professionals
- MeSH terms
- COVID-19, SARS-CoV-2, Generative Artificial Intelligence, Artificial Intelligence, Canada, Education, Medical, Humans, Psychiatry
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