Evidence map›Paper›PMID 40376628›Full record

ReviewIndustrial psychiatry journal

Artificial intelligence in psychiatric education: Enhancing clinical competence through simulation.

Victor Ajluni

Abstract readReview
In one paragraph

Review in Industrial psychiatry journal. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Are Psychiatry Faculty Prepared to Guide AI Use? A National Survey.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026
    Article
  2. Article
  3. Article
  4. Review
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

1 author.

Victor AjluniDepartment of Psychiatry, Wayne State University, Detroit, Michigan, Livonia, MI, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of artificial intelligence (AI) in psychiatric education offers transformative potential to enhance clinical competence through realistic simulations. Traditional educational methods face limitations in replicating complex psychiatric cases, and AI-based tools provide a scalable solution. This narrative review examines current evidence on the efficacy of AI-powered simulations, focusing on their role in skill development, diagnostic accuracy, and safe clinical training. Through a comprehensive literature review of studies from 2010 to 2024, key themes such as AI's ability to standardize patient encounters, provide instant feedback, and improve student confidence are explored. Findings suggest that AI can enhance psychiatric education by offering consistent, adaptable learning experiences that prepare trainees for real-world complexities. However, challenges such as ethical considerations and accessibility disparities must be addressed for AI to be effectively integrated into psychiatric training. This review provides insights into the future of AI in medical education and its potential impact on training the next generation of psychiatrists.

Indexed as

Artificial intelligencemedical educationsimulation based learning

Identifiers

PMID40376628
PMCPMC12077637

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.