Evidence map›Paper›PMID 42591467›Full record

ArticleFrontiers in psychiatry2026

Generative AI in psychiatric education: balancing risks and benefits for responsible integration.

Cong Zhou, Aoxue Zhang, Sen Li

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Cong ZhouSchool of Mental Health, Jining Medical University, Jining, China.
Aoxue ZhangSchool of Mental Health, Jining Medical University, Jining, China.
Sen LiSchool of Mental Health, Jining Medical University, Jining, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative artificial intelligence (GenAI) chatbots, including ChatGPT and DeepSeek, are rapidly reshaping psychiatric education across the continuum from medical students to practicing psychiatrists. These tools offer learners unprecedented opportunities for personalized learning, clinical simulation, and research support. However, their integration also raises substantial risks related to data privacy, content reliability, over reliance on automation, and ethical and regulatory gaps. This perspective argues that neither uncritical adoption nor outright rejection is appropriate. Drawing on emerging evidence and our firsthand experience as psychiatric educators, we propose a balanced framework characterized by three interconnected principles: strong human oversight, comprehensive AI literacy training, and robust institutional governance. We outline five categories of risk, discuss potential benefits specific to psychiatric training, and offer six actionable institutional recommendations. The goal is not to deter innovation but to ensure that GenAI tools enhance rather than replace the clinical judgment, empathy, and professional accountability that lie at the heart of psychiatric practice.

Indexed as

ethicsgenerative AImedical educationmental healthpsychiatric education

Identifiers

PMID42591467
PMCPMC13461632

What OpenQuestion holds

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LicenceCC BY
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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.