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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.
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http://dx.doi.org/10.1016/j.gore.2025.101923 | DOI Listing |
Hernia
September 2025
Center for Perioperative Optimization, Department of Surgery, Copenhagen University Hospital - Herlev and Gentofte, Borgmester Ib Juuls Vej 1, Herlev, DK-2730, Denmark.
Purpose: Primary ventral hernia repair is a common elective procedure; however, mesh placement practices vary widely, and there is limited evidence to guide optimal placement. This international study examined surgeons' preferences and considerations regarding mesh placement in elective primary ventral hernia repair.
Methods: We conducted an international cross-sectional survey targeting surgeons experienced in primary ventral hernia repair.
Climacteric
September 2025
Gynecology Discipline, Obstetrics and Gynecology Department, University of São Paulo School of Medicine, São Paulo, Brazil.
Objective: Social media is an increasingly relevant tool for health education, enabling information exchange, promoting autonomy and supporting informed decision-making. This study introduces Menopausando, a predominantly Portuguese-language digital platform designed to support women during menopausal transition and postmenopause.
Method: This cross-sectional study has been carried out in the Gynecology Discipline, São Paulo University, Brazil, since 2019.
JTCVS Open
August 2025
Department of Cardiothoracic Surgery, Wake Forest University School of Medicine, Winston-Salem, North Carolina.
Background: Social media use among cardiothoracic surgeons has yet to be analyzed. This study aimed to explore how online media utilization by cardiothoracic surgeons differs by subspecialty, sex, geographic region, practice type, level of experience, and training pathway.
Methods: A list of 223 cardiothoracic surgeons was generated by querying the 1066 members of the American Association for Thoracic Surgery and randomly selecting 223 actively practicing surgeons.
Health Inf Sci Syst
December 2025
Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000 China.
Leveraging natural language processing to identify anxiety states from social media has been widely studied. However, existing research lacks deep user-level semantic modeling and effective anxiety feature extraction. Additionally, the absence of clinical domain knowledge in current models limits their interpretability and medical relevance.
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