Evidence map›Paper›PMID 40917135›Full record

ArticleGynecologic oncology reports2025

Scrolling surgeons: Assessment of social media and artificial intelligence usage in gynecologic oncology fellows and fellowship programs.

Jessica Sciuva, Ivana DeVengencie, Jessica Fulton, Molly Morton, Julia Chalif, Eric Rios-Doria, Laura M Chambers

Abstract read
In one paragraph

Article in Gynecologic oncology reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Jessica SciuvaThe Ohio State University College of Medicine, Columbus, OH, United States.
Ivana DeVengencieThe Ohio State University College of Medicine, Columbus, OH, United States.
Jessica FultonDivision of Gynecologic Oncology, The Ohio State University Comprehensive Cancer Center - James Cancer Hospital and Solove Research Institute, Columbus, OH, United States.
Molly MortonDivision of Gynecologic Oncology, The Ohio State University Comprehensive Cancer Center - James Cancer Hospital and Solove Research Institute, Columbus, OH, United States.
Julia ChalifDivision of Gynecologic Oncology, The Ohio State University Comprehensive Cancer Center - James Cancer Hospital and Solove Research Institute, Columbus, OH, United States.
Eric Rios-DoriaDivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Washington, Seattle, WA, United States.
Laura M ChambersDivision of Gynecologic Oncology, The Ohio State University Comprehensive Cancer Center - James Cancer Hospital and Solove Research Institute, Columbus, OH, United States.

Funding

TRAINING IN CANCER BIOLOGY &TRANSPLANTATIONT32CA009515 · NCI · UNIVERSITY OF WASHINGTON · PI NANCY ELLEN DAVIDSON, Effie W Petersdorf · 1985 to 2026
$16.2M
NCI NIH HHS T32 CA009515
6 · The paper itself

Abstract

Objectives: This study examines gynecologic oncology fellows' perceptions and use of social media and artificial intelligence (AI) and their perception of virtual fellowship interviews. Methods: A cross-sectional, IRB-approved survey was distributed to fellows enrolled in ACGME-accredited gynecologic oncology programs across the United States in December 2023. The survey collected demographic data and assessed social media engagement, AI utilization, and perceptions of their applicability in clinical, educational, and professional settings. Results: A total of 36 gynecologic oncology fellows participated. The majority reported using social media for personal purposes (62.5 % strongly agreed, 25.0 % agreed). However, 43.8 % agreed that they used social media for educational purposes, and a significant proportion (43.8 % disagreed, 25.0 % strongly disagreed) did not use social media to promote professional achievements. Most fellows recognized social media's role in patient engagement (50.0 % strongly agreed, 43.8 % agreed) and expressed a desire for more reliable patient-directed content (93.8 % strongly agreed or agreed). While 59.4 % believed social media was useful for fellowship recruitment, it had minimal impact on rank list decisions. Regarding AI, 53.1 % reported using AI tools such as ChatGPT for research (64.7 %) and professional writing (35.3 %), with limited use in patient care. Many fellows (47.1 % strongly agreed, 29.4 % agreed) expressed interest in formal AI training. Conclusions: Gynecologic oncology fellows primarily use social media for personal rather than professional purposes. AI is emerging as a research tool, though concerns persist regarding its application in patient care. Formal training in social media and AI could enhance fellows' ability to integrate these tools practice.

Indexed as

Artificial intelligenceEducationGynecologic oncologyGynecologic oncology fellowshipSocial media

Identifiers

PMID40917135
PMCPMC12410171

What OpenQuestion holds

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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.