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

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.

Authors

Institutions

Publication Details

Journal
Frontiers in Medicine
Published
2026-04-28
DOI
https://doi.org/10.3389/fmed.2026.1817855
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Ziyan Hu, Dan Huang, C X R CHEN, Shujing Xie et al.
Frontiers in Medicine
Artificial Intelligence in Healthcare and Education
article

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

Ziyan Hu, Dan Huang, C X R CHEN, Shujing Xie, Zhihui Luo, Xiaohong Du, Shengliang Peng, Haijun Hu
article en

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.

Frontiers in MedicineVol. 13
Nanchang University (CN), Queen Mary University of London (GB), Queen Mary Hospital (CN), First Affiliated Hospital of Jiangxi Medical College (CN), Key Laboratory of Guangdong Province (CN), Second Affiliated Hospital of Nanchang University (CN)
Quality Education
Openalex Percentile: Top 13%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.