Evidence map›Paper›PMID 41960068›Full record

ArticleJTCVS open2026

Gender-based differences in perceptions of artificial intelligence in clinical cardiothoracic surgery.

Joseph J Platz, Darren S Bryan, Keith S Naunheim, Mark K Ferguson

Abstract read
In one paragraph

Article in JTCVS open, 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

4 authors.

Joseph J PlatzDivision of Cardiothoracic Surgery, Department of Surgery, St. Louis University, St. Louis, Mo.
Darren S BryanSection of Thoracic Surgery, Department of Surgery, University of Chicago, Chicago, Ill.
Keith S NaunheimDivision of Cardiothoracic Surgery, Department of Surgery, St. Louis University, St. Louis, Mo.
Mark K FergusonSection of Thoracic Surgery, Department of Surgery, University of Chicago, Chicago, Ill.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) is poised to reshape cardiothoracic (CT) surgery. Prior studies suggest different attitudes and adoption rates of AI based on gender. This study explores these gender-based differences further to identify domains in which differences might exist. Methods: A survey addressing the use of AI in CT surgery was created and distributed to members of multiple international CT surgery societies. Results were collected electronically. Responses were scored on a 5-point Likert scale and then collapsed into 3 categories for χ Results: Data from 412 CT surgeons were analyzed, including 344 men and 68 women specializing in cardiac (n = 213) and thoracic (n = 176) surgery. Geographic representation included North America (65%), Europe (24%), and Asia (11%). In comparing subspecialties, female CT surgeons were more likely than male surgeons to assign a limited role to AI. North American female surgeons saw the smallest role for AI. Female surgeons' hopes for AI focused on reductions in administrative burden and efficiency gains, while men sought improved diagnostic accuracy and decision making support. Women were worried about systemic biases and clinical dehumanization, while men were concerned with the technical implementation of AI. Conclusions: Gender is associated with CT surgeons' perceptions of AI in clinical care, with women, particularly thoracic subspecialists, expressing more reservations than men. It is important to understand these different views, as adoption of AI in CT surgery may have an important impact on gender equity in the field.

Indexed as

AIartificial intelligencecardiothoracic surgerylarge language modelssurvey

Identifiers

PMID41960068
PMCPMC13059977

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.