Evidence map›Paper›PMID 42716991›Full record

ArticleScientific reports2026

Generative AI in medicine: use and acceptance among professionals in orthopedics and traumatology: a survey-based analysis in Germany.

Philipp Zehnder, Frederik Aasen-Hartz, Michael Zyskowski, Conrad Ketzer, Tobias Resch, AG Digitalisierung der DGOU, Peter Biberthaler, Dominik Pförringer

Abstract read
In one paragraph

Article in Scientific reports, 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

8 authors.

Philipp ZehnderDepartment of Trauma Surgery, Klinikum rechts der Isar, TUM University Clinic, Technical University Munich, Ismaningerstr. 22, 81675, Munich, Germany. philipp.zehnder@mri.tum.de.ORCID https://orcid.org/0000-0002-6797-0453
Frederik Aasen-HartzDepartment of Trauma Surgery, Klinikum rechts der Isar, TUM University Clinic, Technical University Munich, Ismaningerstr. 22, 81675, Munich, Germany.
Michael ZyskowskiDepartment of Trauma Surgery, Klinikum rechts der Isar, TUM University Clinic, Technical University Munich, Ismaningerstr. 22, 81675, Munich, Germany.
Conrad KetzerDepartment of Trauma Surgery, Klinikum rechts der Isar, TUM University Clinic, Technical University Munich, Ismaningerstr. 22, 81675, Munich, Germany.
Tobias ReschDepartment of Trauma Surgery, Klinikum rechts der Isar, TUM University Clinic, Technical University Munich, Ismaningerstr. 22, 81675, Munich, Germany.
AG Digitalisierung der DGOU
Peter BiberthalerDepartment of Trauma Surgery, Klinikum rechts der Isar, TUM University Clinic, Technical University Munich, Ismaningerstr. 22, 81675, Munich, Germany.
Dominik PförringerDepartment of Trauma Surgery, Klinikum rechts der Isar, TUM University Clinic, Technical University Munich, Ismaningerstr. 22, 81675, Munich, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative AI tools such as ChatGPT (OpenAI, San Francisco, CA, USA) are showing remarkable capabilities in generating qualitative text, displaying potential to revolutionize daily life and professional practices, including medical diagnostics and education. The use of ChatGPT in publishing, especially its legal and ethical implications, is subject of growing scientific interest and discussion. The underlying study aims to understand the current status, usage patterns, and future expectations of generative AI among healthcare professionals. An anonymous online survey among healthcare professionals was distributed to members of the German society for orthopedics and traumatology (February and March 2024). The questionnaire comprised 12 questions regarding individual use of and attitude towards artificial intelligence. The responses of two age-dependent groups (group 1 ≤ 45 years vs. group 2 > 45 years) were compared. A total of 375 of 504 invited participants completed the survey (response rate 74%); 177 respondents were aged ≤ 45 years. Awareness of ChatGPT was high in both groups, while prior self-reported use was more common in group 1 (73% vs. 62%, p = 0.03). Respondents aged ≤ 45 years more often reported that they could imagine using ChatGPT for manuscript-related tasks (59% vs. 44%, p = 0.001), whereas respondents aged > 45 years more frequently supported disclosure of ChatGPT use in manuscript preparation (86% vs. 77%, p = 0.001). The age groups differed significantly in professional role distribution. In an adjusted analysis including professional position, the association between age group and AI use remained similar but not significant (unadjusted OR 1.6, 95% CI 1.1-2.4; adjusted OR 1.6, 95% CI 1.0-2.6; p 0.053). In this cohort of DGOU members, awareness of generative AI was high and self-reported use was common. Younger respondents reported more frequent use of ChatGPT and related digital tools. However, these findings should be interpreted cautiously because they reflect self-reported cross-sectional data from one professional society and do not demonstrate actual integration of AI into clinical or academic workflows. The creation of an ethical framework is therefore essential and our participants want the use to be clearly identified.

Indexed as

Generative Artificial IntelligenceHealth PersonnelOrthopedicsTraumatologyAdultAttitude of Health PersonnelFemaleGermanyHumansMaleMiddle AgedSurveys and QuestionnairesAI in scientific writingChatGPTDigital health toolsEthics in AI useGenerative AImedical publishing

Identifiers

PMID42716991
PMCPMC13558731

What OpenQuestion 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 OpenQuestion graph.