Evidence map›Paper›PMID 41987144›Full record

ArticleBMC medical education2026

Current practices and perceptions of ChatGPT in gynecologic oncology: results from a cross-sectional questionnaire (TRSGO-AI-001).

İbrahim Yalçın, Salih Taşkın, Gözde Şahin, Faruk Köse, Fırat Ortaç, Esra Bilir

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Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

İbrahim YalçınDepartment of Gynecology and Obstetrics, Dokuz Eylül University, Izmir, Türkiye.
Salih TaşkınDivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, Ankara University School of Medicine, Ankara, Türkiye.
Gözde ŞahinDepartment of Gyneacologic Oncology, University of Health Sciences, Cam Sakura City Hospital, Istanbul, Türkiye.
Faruk KöseDepartment of Obstetrics and Gynecology, Acıbadem Mehmet Ali Aydinlar University, İstanbul, Türkiye.
Fırat OrtaçDivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, Ankara University School of Medicine, Ankara, Türkiye.
Esra BilirDepartment of Gynecology and Obstetrics, University Medical Center Göttingen, Robert-Koch-Straße 40, Göttingen, 37075, Germany. esra.bilir@med.uni-goettingen.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe aimed to assess current practices, perceptions, and perceived effectiveness of ChatGPT among gynecologic oncology professionals in academic and clinical settings.

methodsA 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.

resultsOur 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).

conclusionsChatGPT 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.

Indexed as

Attitude of Health PersonnelGynecologyMedical OncologyAdultCross-Sectional StudiesFemaleGenerative Artificial IntelligenceGenital Neoplasms, FemaleHumansLarge Language ModelsMiddle AgedSurveys and QuestionnairesChatGPTGynecologic oncologyLarge language modelsOpenAIPhysician

Identifiers

PMID41987144
PMCPMC13081464

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.