Evidence map›Paper›PMID 42543452›Full record

ArticleJournal of cancer education : the official journal of the American Association for Cancer Education2026

Perceptions of Artificial Intelligence in Gynecologic Oncology Education: Insights from a Qualitative Study Using Semi-Structured Interviews.

Jianye Wang, Jiaqi Xu, Zhaojing Fu, Li Song

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Article in Journal of cancer education : the official journal of the American Association for Cancer 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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0citing papers in PubMed
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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

4 authors.

Jianye WangDepartment of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China.
Jiaqi XuDepartment of Gynecology and Obstetrics, Tianjin Medical University General Hospital, Tianjin, 300052, China.
Zhaojing FuDepartment of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China.
Li SongDepartment of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China. sl0930china@sdu.edu.cn.

Funding

Natural Science Foundation of Shandong Province ZR2025MS1305The Undergraduate Education and Teaching Reform Research Project of Shandong University 2024Y036
6 · The paper itself

Abstract

Training in gynecologic oncology requires mastery of complex decision making and technically demanding procedures, yet opportunities for repetitive practice are often limited by case volume, patient-safety considerations, and unequal access to expert supervision. Artificial intelligence (AI) has therefore attracted attention as a potential adjunct to simulation-based and feedback-intensive surgical education. This qualitative study explored how educators and learners perceive the role of AI in gynecologic oncology training. Semi-structured interviews were conducted with five gynecologic oncology faculty members and ten postgraduate obstetrics and gynecology trainees at a university-affiliated teaching hospital. Interview data were analyzed using inductive thematic analysis. Three themes were identified: AI as a tool for expanding deliberate practice and structured feedback, barriers that limit acceptable adoption, and conditions required for responsible implementation. Participants described AI-enhanced simulation as valuable for risk-free rehearsal, exposure to uncommon cases, and more objective assessment, while also emphasizing concerns about realism, cost, access, and data governance. Both groups supported a hybrid educational model in which AI supplements rather than replaces human mentorship. These findings suggest that AI can strengthen gynecologic oncology education when it is integrated into competency-based curricula, accompanied by faculty oversight, and supported by appropriate ethical and infrastructural safeguards.

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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.