Evidence map›Paper›PMID 41910823›Full record

ArticleLangenbeck's archives of surgery2026

An Australian perspective of using video for the assessment of laparoscopic surgery and support for artificial intelligence in performance evaluation.

Yuchen Luo, Shekhar Kumta, Wanda Stelmach, Russell Hodgson

Abstract read
In one paragraph

Article in Langenbeck's archives of 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

4 authors.

Yuchen LuoDivision of Surgery, Northern Health, Epping, Australia. Frank.Luo@student.unimelb.edu.au.
Shekhar KumtaDivision of Surgery, Northern Health, Epping, Australia.
Wanda StelmachDivision of Surgery, Northern Health, Epping, Australia.
Russell HodgsonDivision of Surgery, Northern Health, Epping, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe objective evaluation of technical competence in laparoscopic surgery is critical, yet many current assessment tools lack precision and video-based assessment is not routinely integrated into formal training. This survey gauges a national consensus on video-based assessment of cholecystectomy and the perspectives of use of artificial intelligence in the future.

methodsThis cross-sectional survey study utilized a national survey distributed to the Australian general surgery community via the REDCap platform. The survey assessed proposed criteria for the dissection and excision phases of laparoscopic cholecystectomy and explored surgeons’ perspectives on the future use of video recordings and artificial intelligence in surgical training.

resultsWith a 20.0% response rate (192/962), the survey revealed a strong consensus among surgeons regarding the proposed assessment criteria, with key items like non-targeted diathermy burning and incorrect clipping achieving high agreement. While video recording for review is not routine, a significant proportion of participants expressed that video-based assessment is “somewhat likely” or “very likely” to become mandatory for competency evaluation. Attitudes toward integrating AI and software tools for video-based assessment were generally positive.

conclusionThis study demonstrates a clear consensus among general surgeons on objective assessment criteria for laparoscopic cholecystectomy and signals a shift towards formal video-based assessment and AI integration in surgical training. These findings are crucial for developing reliable assessment tools and integrating advanced technologies to enhance surgical education and trainee evaluation.

Indexed as

Artificial IntelligenceCholecystectomy, LaparoscopicClinical CompetenceVideo RecordingAustraliaCross-Sectional StudiesFemaleHumansSurveys and QuestionnairesArtificial intelligenceComputer visionLaparoscopic cholecystectomyLaparoscopySurgical educationSurgical trainingVideo assessmentVideo recording

Identifiers

PMID41910823
PMCPMC13156208

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.