Evidence map›Paper›PMID 42131594›Full record

ReviewFrontiers in medicine2026

Artificial intelligence in anesthesiology education: transformative applications, challenges, and future perspectives.

Cheng Chen, Shujing Xie, Zhihui Luo, Ziyan Hu, Xiaohong Du, Dan Huang, Haijun Hu, Shengliang Peng

Abstract readReview
In one paragraph

Review in Frontiers in medicine, 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

8 authors.

Cheng ChenDepartment of Anesthesiology, the Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China.
Shujing XieDepartment of Anesthesiology, the Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China.
Zhihui LuoDepartment of Anesthesiology, the Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China.
Ziyan HuDepartment of Anesthesiology, the Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China.
Xiaohong DuDepartment of Anesthesiology, the Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China.
Dan HuangDepartment of Anesthesiology, the Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China.
Haijun HuDepartment of Anesthesiology, the Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China.
Shengliang PengDepartment of Anesthesiology, the Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence offers the potential to revolutionize anesthesiology education by enabling precision education, a data-driven approach to tailor learning experiences to individual needs, thereby moving beyond the constraints of traditional pedagogical methods. This review examines the emerging applications and potential impact of AI-driven technologies, from virtual reality simulators that facilitate deliberate practice of complex procedures to machine learning platforms that enable precision education and objective competency assessment. We highlight how these tools enhance procedural fluency, clinical reasoning, and educational management. Nevertheless, this technological advancement is accompanied by profound challenges, including the risks of de-skilling, the perpetuation of algorithmic biases, data security vulnerabilities, and issues of equitable access. We argue that AI's role is as an augmentative tool, empowering educators to provide more personalized feedback and facilitate higher-order skill development, while also raising crucial ethical considerations. Navigating the future of anesthesiology education requires a balanced approach: embracing the benefits of AI while implementing robust governance to mitigate its risks, thereby fostering a new generation of anesthesiologists equipped to leverage technology for superior patient care. To this end, future research should prioritize rigorous validation of AI tools in clinical settings and focus on ethical guidelines for responsible AI implementation.

Indexed as

anesthesiology educationartificial intelligenceimmersive learningobjective competency assessmentprecision medical education

Identifiers

PMID42131594
PMCPMC13160743

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.