Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Current practices and perceptions of ChatGPT in gynecologic oncology: results from a cross-sectional questionnaire (TRSGO-AI-001)

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 Background We aimed to assess current practices, perceptions, and perceived effectiveness of ChatGPT among gynecologic oncology professionals in academic and clinical settings. Methods A 23-item, international online survey was conducted between July and September 2025. Gynecologic oncology professionals were recruited via social media and snowball sampling. We evaluated demographics, awareness, utilization, and perceptions of ChatGPT-3.5 across different domains. Results Our respondents(n = 111) were predominantly aged 36–45 years(55.0%), from Türkiye (90.1%), and affiliated with academic institutions(87.4%). Nearly all respondents(99.1%) were aware of ChatGPT, and 66.7% reported professional use, primarily for literature summarization(52.3%), teaching(42.3%), and academic writing(36.9%). Direct clinical use remained somewhat limited(17.1%), with minimal engagement in patient communication(7.2%). Misinformation was the most commonly reported concern(66.7%). Perceived effectiveness was highest in academic writing, literature summarization, and teaching, whereas patient communication, clinical decision support, and exam preparation were rated somewhat low. Overall, 60.3% indicated that ChatGPT contributed meaningfully to their work, 45.9% supported its integration into gynecologic oncology education, and 58.5% would recommend it to colleagues. Participants < 45 years and those with < 5 years of clinical experience reported significantly more frequent ChatGPT use in their professional activities(p-value = 0.026). Those with greater clinical experience were more likely to use ChatGPT for writing purposes(p-value = 0.039), whereas those < 45 years used it more often for clinical decision-making(p-value = 0.015). Conclusions ChatGPT is widely recognized and used for academic and educational tasks in gynecologic oncology, yet its clinical applications remain limited. Future research should optimize large language models for clinical use, evaluate comparative outcomes across diverse models, and investigate their integration into multidisciplinary care. Supplementary Information The online version contains supplementary material available at 10.1186/s12909-026-09192-w. Keywords: Large language models, ChatGPT, OpenAI, Gynecologic oncology, Physician

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. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. 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. Blind-score reasoning, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. 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

İbrahim Yalçın; Salih Taşkın; Gözde Şahin; Faruk Köse; Fırat Ortaç; Esra Bilir. Current practices and perceptions of ChatGPT in gynecologic oncology: results from a cross-sectional questionnaire (TRSGO-AI-001). BMC Medical Education (2026-04-15). https://doi.org/10.1186/s12909-026-09192-w

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.