Evidence map›Paper›PMID 42115373›Full record

ArticleSurgical endoscopy2026

Surgeons' perceptions of artificial intelligence (AI) for gaze guidance in laparoscopic cholecystectomy.

Taylor Quinn, Elena Lopez, Joseph C L'Huillier, John M Woodward, Manohar Golleru, Rachel Clipp, Andinet Enquobahrie, Lora Cavuoto, Steven D Schwaitzberg

Abstract read
In one paragraph

Article in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

9 authors.

Taylor QuinnDepartment of Industrial & Systems Engineering, University at Buffalo, 210 Talbert Way, Buffalo, NY, 14260, USA. tlquinn@buffalo.edu.ORCID http://orcid.org/0000-0002-6589-7475
Elena LopezJacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
Joseph C L'HuillierDepartment of Surgery, University at Buffalo, Buffalo, NY, USA.
John M WoodwardDepartment of Surgery, University at Buffalo, Buffalo, NY, USA.
Manohar GolleruDepartment of Industrial & Systems Engineering, University at Buffalo, 210 Talbert Way, Buffalo, NY, 14260, USA.
Rachel ClippKitware, Inc., Clifton Park, NY, USA.
Andinet EnquobahrieKitware, Inc., Clifton Park, NY, USA.
Lora CavuotoDepartment of Industrial & Systems Engineering, University at Buffalo, 210 Talbert Way, Buffalo, NY, 14260, USA.
Steven D SchwaitzbergDepartment of Surgery, University at Buffalo, Buffalo, NY, USA.

Funding

CTSA UM1 State University of New York at BuffaloUM1TR005296 · NCATS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI Sanjay Sethi · 2025 to 2026
$8.3M
Surgical Simulator for Improving Skill Proficiency and ResilienceR01EB031808 · NIBIB · KITWARE, INC. · PI ENQUOBAHRIE, ANDINET ASMAMAW · 2021 to 2024
$2.2M
NCATS NIH HHS R01EB031808NCATS NIH HHS UM1 TR005296NIBIB NIH HHS R01 EB031808
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) tools offer new opportunities to support human perception and provide gaze guidance in surgery. However, there remains a need to better understand surgeons' perceptions of these tools and to identify desired features across levels of expertise. This study explored surgeons' perceptions of AI-based gaze guidance to determine how such tools can best meet surgeons' needs while recognizing their limitations.

methodsSeventy-one surgeons (38 attendings, 33 residents) watched laparoscopic cholecystectomy videos and responded to open-ended questions about their perceptions of AI for gaze guidance in surgery. Responses were analyzed using latent content analysis to identify themes and desired AI features. Each response was also categorized as positive, negative, or neutral to assess overall perception.

resultsAttendings (92%) and residents (82%) were positive about AI for visual gaze guidance. Neutral responses (9.8%) reflected uncertainty or limited familiarity with AI. Desired features included directing attention, identification, highlighting and visualization, feedback, decision support, educational tools, communication and collaboration, and planning and mapping. Identification involved AI support for recognizing critical structures, abnormalities, gaps in attention, and spatial orientation. Attendings favored AI for decision support and feedback, while residents emphasized its value for visualization and education. Surgeons frequently described AI as a "second pair of eyes," providing guidance, visualization, user control, and safety. Design considerations were also identified to provide guidance without being a potential distractor to the surgeon.

conclusionSurgeons across expertise levels expressed positive views toward AI support for gaze guidance, emphasizing the need for features that complement human expertise, align with individual training needs, and account for contextual factors such as minimizing distractions and preventing tunnel vision. Identification, direct attention, and decision support were the most desired features. The findings suggest that tailored AI-based tools can play an important role in surgical training and active procedures by supporting harm reduction.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelCholecystectomy, LaparoscopicSurgeonsAdultClinical CompetenceFemaleHumansIntelligent SystemsInternship and ResidencyMaleArtificial intelligenceGaze guidanceLaparoscopic cholecystectomyPerception

Identifiers

PMID42115373
PMCPMC13644800

What OpenQuestion holds

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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.