Work

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

Year: 2025
Type: article
Study design: Observational Study
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.
Language: English
Subfield: Health
Open Access status: gold
Estimated APC paid: $930
OpenAlex ID: W4413290651
FWCI: 1.074
Cites: 18
Cited by: 1
Related to: 10