Evidence map›Paper›PMID 42166094›Full record

ArticleUpdates in surgery2026

Defining the artificial intelligence knowledge gap in surgery: experience and perspectives from surgical resident and postgraduate.

Zhongshang Dai, Zhuoyuan Chen, Liang Weng, Lin Guo, Zhehao Dai

Abstract read
PubMed Publisher
In one paragraph

Article in Updates in surgery, 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

5 authors.

Zhongshang DaiDepartment of Infectious Diseases, The Second Xiangya Hospital, Central South University, Changsha, 410011, China.
Zhuoyuan ChenDepartment of Orthopedics, Yan'an Hospital Affiliated to Kunming Medical University, Kunming, 650051, China.
Liang WengDepartment of Pathology, School of Basic Medical Sciences, Peking University Third Hospital, Peking University Health Science Center, Beijing, 100191, China.
Lin GuoThe Big Data Institute, Central South University, Changsha, 410083, China.
Zhehao DaiDepartment of Spine Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, China. f2daizhehao@csu.edu.cn.ORCID http://orcid.org/0000-0001-9285-2494

Funding

Graduate Education and Teaching Reform Project of Central South University 2024JGB087Soft Science Research Project of Changsha kh2502020
6 · The paper itself

Abstract

Despite the growing prominence of Artificial Intelligence (AI) in surgical practice, surgical residents and postgraduates receive limited formal training. A cross-sectional survey of 322 surgical residents and postgraduates from three Chinese medical universities assessed their knowledge, perceptions, and experience regarding AI in surgical practice. Among respondents, 76.7% reported prior experience with clinical AIrelated applications and generally recognized their value. However, self-reported preparedness to critically appraise AI and use it responsibly was modest or low. In particular, over 75% of respondents expressed limited confidence in evaluating model reliability and in identifying bias or other limitations. 85.7% supported the formal integration of AI into surgical training programs. On the other hand, 267 respondents reported that they had not received any formal AI training. Interest in AI-related topic differed significantly across training stages (χ

Indexed as

Artificial intelligenceMedical education researchPostgraduateSelf-assessmentSurgery

Identifiers

PMID42166094

What Socratic holds

Textmetadata
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 Socratic graph.