Use and Perceptions of Artificial Intelligence Among Respondents to a Self-Selected Survey of Obstetrician-Gynecologists in France
MARTINEAU, R.; COMBES, C.; YAUY, K.; DERUELLE, P.
Show abstract
Background: Artificial intelligence (AI) is increasingly used in healthcare, but data on its real-world use and perceptions among obstetricians and gynecologists remain limited. Objectives: To assess familiarity, use, perceptions, and expectations regarding AI among physicians practicing obstetrics and gynecology in France. Study Design: We conducted a cross-sectional survey between June 2 and July 30, 2025, mainly distributed through professional organizations or associations and social media. Participation was voluntary and self-selected. AI use was defined as any use of an AI tool in routine clinical practice, regardless of type or frequency. Comparisons were made between AI users and non-users and between junior and senior physicians. Results: Among 469 respondents, representing approximately 5% of the target population, 199 of 466 (42.7%) reported AI use in practice; three respondents were excluded from this analysis because of discordant questionnaire responses. AI use was associated with male gender and professional status, with residents more represented among users. Junior physicians reported greater AI use than senior physicians and expressed greater concern regarding medical confidentiality. Current applications primarily involved administrative tasks, medical writing, literature retrieval, and translation; anticipated future uses centered on clinical decision support and medical data interpretation. Perceptions of several AI applications differed between groups. Conclusions: AI was reported to be used by a substantial proportion of respondents, primarily for supportive and informational tasks. These findings support further evaluation of training, validation, governance, and safe implementation of AI in obstetrics and gynecology.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Medical students' perceptions towards artificial intelligence in education and practice: A multinational, multicenter cross-sectional study 94%
- Large language models for generating medical examinations: systematic review 94%
- Evaluation of Statistical Illiteracy in Latin American Clinicians and of the Efficacy of a 10-Hour Course 92%
Similar papers in this journal
- Translation, adaptation and psychometric evaluation of the German version of the Abortion Attitude Scale - a secondary analysis of a cross-sectional study among medical students 93%
- Evaluating user experience with immersive technology in simulation-based education: a modified Delphi study with qualitative analysis 91%
- Telehealth during and beyond the COVID-19 Pandemic: Evidence from Licensed Dietitians in an Emerging Economy 91%
Similar papers in this journal
- Early medical abortion using telemedicine – acceptability to patients 92%
- Why women choose abortion through telemedicine outside the formal health sector in Germany? A mixed-methods study 92%
- Demand for Self-Managed Online Telemedicine Abortion in Eight European Countries During the COVID-19 Pandemic: A Regression Discontinuity Analysis 91%
Similar papers in this journal
- Development and Evaluation of MADDIE: Method to Acquire Delivery Date Information from Electronic Health Records 92%
- Synthetic Data Generation in Healthcare: A Scoping Review of reviews on domains, motivations, and future applications 92%
- Performance of Advanced Large Language Models (GPT-4o, GPT-4, Gemini 1.5 Pro, Claude 3 Opus) on Japanese Medical Licensing Examination: A Comparative Study 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.