Generative AI · Education leadership

Actionable companion · Governance, equity and accountability

From evidence to district action: Generative AI in medicine: use and acceptance among professionals in orthopedics and traumatology: a survey-based analysis in Germany

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

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Evidence and context

Health-professions education context: test transfer to K–12 rather than assuming it.

Original authors’ abstract, reproduced under the linked source license:

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. Supplementary Information The online version contains supplementary material available at https://doi.org/10.1038/s41598-026-70337-4. Keywords: AI in scientific writing, ChatGPT, Digital health tools, Ethics in AI use, Generative AI, medical publishing Subject terms: Health care, Mathematics and computing, Medical research

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Convene a teacher, student, family and technology working group. Test one proposed use against a documented privacy, accessibility, bias and human-review checklist before authorizing a pilot. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Track unresolved risks, subgroup access, complaint resolution time and the proportion of outputs that require substantive human correction. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Philipp Zehnder; Frederik Aasen-Hartz; Michael Zyskowski; Conrad Ketzer; Tobias Resch; AG Digitalisierung der DGOU; Peter Biberthaler; Dominik Pförringer. Generative AI in medicine: use and acceptance among professionals in orthopedics and traumatology: a survey-based analysis in Germany. Scientific Reports (2026-09-09). https://doi.org/10.1038/s41598-026-70337-4

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.