Evidence map›Paper›PMID 42521959›Full record

ArticleAcademic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry2026

Artificial Intelligence in Psychiatric Graduate Medical Education.

Manal Khan, Juliet Edgcomb, Jonathan Heldt, Yvonne Yang, Katrina Debonis, Misty Richards, Sahib S Khalsa

Abstract readEditorial
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In one paragraph

Article in Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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
    Article
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

7 authors.

Manal KhanDavid Geffen School of Medicine at University of California Los Angeles (UCLA) and UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA.
Juliet EdgcombDavid Geffen School of Medicine at University of California Los Angeles (UCLA) and UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA.
Jonathan HeldtDavid Geffen School of Medicine at University of California Los Angeles (UCLA) and UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA.
Yvonne YangDavid Geffen School of Medicine at University of California Los Angeles (UCLA) and UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA.
Katrina DebonisDavid Geffen School of Medicine at University of California Los Angeles (UCLA) and UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA.
Misty RichardsDavid Geffen School of Medicine at University of California Los Angeles (UCLA) and UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA.
Sahib S KhalsaDavid Geffen School of Medicine at University of California Los Angeles (UCLA) and UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA. skhalsa@mednet.ucla.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivePsychiatry graduate medical education (GME) faces converging pressures of increasing clinical demand, rising administrative burden, and workforce burnout. The emergence of artificial intelligence (AI), particularly large language models (LLMs), offers new possibilities for expanding educational capacity and personalizing training. However, the relational and narrative foundations of psychiatric practice create unique challenges for responsible integration. This review defines opportunities and risks of AI across key domains of psychiatry GME.

methodsPsychiatric education leaders applied the Josiah Macy Foundation's framework for AI in medical education to four domains of psychiatry GME: recruitment, didactic development and clinical learning, assessment and feedback, and program evaluation.

resultsAI has potential to reduce administrative burden, augment clinical reasoning instruction, enable more consistent formative assessment, and support data-driven program improvement. Risks include overdependence, erosion of documentation and formulation skills, bias amplification, privacy vulnerabilities, and weakening of the therapeutic and supervisory relationships central to psychiatric training.

conclusionsResponsible AI integration in psychiatry GME requires staged introduction aligned with trainee developmental level, faculty engagement, human oversight in evaluation and decision-making, transparent communication of AI use, robust data governance, and prioritization of tools that deepen rather than replace human connection. Thoughtful implementation can support, rather than supplant, the relational and reflective practices at the heart of psychiatric education.

Indexed as

Artificial intelligenceLarge language modelsPsychiatric graduate medical education

Identifiers

PMID42521959

What OpenQuestion holds

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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.