Evidence map›Paper›PMID 40720804›Full record

ArticleJMIR medical education2025

Applications of Artificial Intelligence in Psychiatry and Psychology Education: Scoping Review.

Julien Prégent, Van-Han-Alex Chung, Inès El Adib, Marie Désilets, Alexandre Hudon

Abstract readScoping Review
In one paragraph

Article in JMIR medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Strengths and Potential Pitfalls of the Use of Artificial Intelligence in Psychiatric Education and Practice.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Julien PrégentDepartment of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada.ORCID 0009-0006-0200-5376
Van-Han-Alex ChungFaculty of Medicine, McGill University, Montreal, QC, Canada.ORCID 0009-0004-4649-9887
Inès El AdibDepartment of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada.ORCID 0009-0005-6497-9857
Marie DésiletsDepartment of Psychiatry, Institut universitaire en santé mentale de Montréal, Montréal, QC, Canada.ORCID 0009-0001-6015-3178
Alexandre HudonDepartment of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada.ORCID 0000-0002-4868-0928

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is increasingly integrated into health care, including psychiatry and psychology. In educational contexts, AI offers new possibilities for enhancing clinical reasoning, personalizing content delivery, and supporting professional development. Despite this emerging interest, a comprehensive understanding of how AI is currently used in mental health education, and the challenges associated with its adoption, remains limited.

objectiveThis scoping review aimed to identify and characterize current applications of AI in the teaching and learning of psychiatry and psychology. It also sought to document reported facilitators of and barriers to the integration of AI within educational contexts.

methodsA systematic search was conducted across 6 electronic databases (MEDLINE, PubMed, Embase, PsycINFO, EBM Reviews, and Google Scholar) from inception to October 2024. The review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Studies were included if they focused on psychiatry or psychology, described the use of an AI tool, and discussed at least 1 facilitator of or barrier to its use in education. Data were extracted on study characteristics, population, AI application, educational outcomes, facilitators, and barriers. Study quality was appraised using several design-appropriate tools.

resultsFrom 6219 records, 10 (0.2%) studies met the inclusion criteria. Eight categories of AI applications were identified: clinical decision support, educational content creation, therapeutic tools and mental health monitoring, administrative and research assistance, natural language processing (NLP), program/policy development, students' study aid, and professional development. Key facilitators included the availability of AI tools, positive learner attitudes, digital infrastructure, and time-saving features. Barriers included limited AI training, ethical concerns, lack of digital literacy, algorithmic opacity, and insufficient curricular integration. The overall methodological quality of included studies was moderate to high.

conclusionsAI is being used across a range of educational functions in psychiatry and psychology, from clinical training to assessment and administrative support. Although the potential for enhancing learning outcomes is clear, its successful integration requires addressing ethical, technical, and pedagogical barriers. Future efforts should focus on AI literacy, faculty development, and institutional policies to guide responsible and effective use. This review underscores the importance of interdisciplinary collaboration to ensure the safe, equitable, and meaningful adoption of AI in mental health education.

Indexed as

Artificial IntelligencePsychiatryPsychologyHumansartificial intelligencechatbotclinical decision supportdigital healthe-learningmachine learningmedical educationmental health trainingpsychiatry educationpsychology education

Identifiers

PMID40720804
PMCPMC12340458

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.