Evidence map›Paper›PMID 41869486›Full record

ArticleJB & JS open access

Global Perspectives on Artificial Intelligence in Orthopaedic Surgery: Findings from the 2025 ABC Travelling Fellowship Survey.

Prism S Schneider, Madeleine Gorman-Asal, Ravi Bheeshma, Melvin D Helgeson, Xinning Li, Peter C Rhee, Miho J Tanaka, John Scolaro

Abstract read
In one paragraph

Article in JB & JS open access. 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. Article
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

8 authors.

Prism S SchneiderDepartment of Surgery, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.ORCID https://orcid.org/0000-0002-2653-6503
Madeleine Gorman-AsalDepartment of Surgery, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.ORCID https://orcid.org/0009-0003-2944-7340
Ravi BheeshmaDivision of Orthopaedic Surgery, Sunnybrook Holland Orthopaedic & Arthritic Centre, University of Toronto, Toronto, Ontario, Canada.ORCID https://orcid.org/0000-0003-1829-6885
Melvin D HelgesonDepartment of Orthopaedic Surgery, Mayo Clinic, Rochester, Minnesota.ORCID https://orcid.org/0000-0003-0907-1668
Xinning LiDepartment of Orthopaedic Surgery, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts.ORCID https://orcid.org/0000-0002-7577-847X
Peter C RheeDepartment of Orthopaedic Surgery, Mayo Clinic, Rochester, Minnesota.ORCID https://orcid.org/0000-0003-0530-4225
Miho J TanakaDepartment of Orthopaedics, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.ORCID https://orcid.org/0000-0001-6489-3803
John ScolaroDepartment of Orthopaedic Surgery, University of California, Irvine, Irvine, California.ORCID https://orcid.org/0000-0001-7926-5017

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Successful applications of artificial intelligence (AI) in healthcare have increased interest in how it could be integrated into orthopaedic surgery. However, orthopaedics surgeons' current use of AI and attitudes toward its incorporation remain largely unexplored. The Methods: The survey was administered from May 7 to September 4, 2025 to orthopaedic surgeons across Australia, New Zealand, the United Kingdom, and North America. Questions addressed demographics, AI training and use, and attitudes toward AI. Descriptive statistics summarized responses, chi-square tests compared AI use by geography and attitudes by career stage, and linear regression explored associations between demographics, AI behaviors, and outlook on AI. Results: Among 350 participants, most were trainees (45.1%) and from North America (42.9%). AI use exceeded training, and 79.9% rated their AI knowledge as average or poor. Trainees reported significantly more favorable attitudes toward AI than late-career respondents. Higher self-rated AI knowledge and AI use in clinical and research settings were associated with a more positive outlook on the future of AI in orthopaedics. Conclusions: These findings highlight the need for formal AI education, as use is widespread despite limited training, particularly among later-career surgeons. Future studies should reduce self-selection and career-stage imbalance and include additional demographic variables to improve generalizability. Level of Evidence: III. See Instructions for Authors for a complete description of levels of evidence.

Identifiers

PMID41869486
PMCPMC13004211

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.