Accepted for/Published in: JMIR Formative Research
Date Submitted: Apr 3, 2026
Date Accepted: Aug 14, 2026
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Assessing the Readiness of Obstetrics and Gynaecology Trainees for the Introduction of Artificial Intelligence in Clinical Practice: A Cross-Sectional Study
ABSTRACT
Background:
Artificial intelligence technologies aim to create systems that mimic human intelligence for tasks such as problem-solving, learning, and language understanding, and they have been increasingly introduced in various fields of obstetrics and gynaecology.
Objective:
Report on the readiness of obstetrics and gynaecology trainees for introducing artificial intelligence to clinical practice, and identify predictors of low readiness.
Methods:
A cross-sectional study between 1st and 31st of December 2024 that included obstetrics and gynaecology trainees. Data were collected on trainees’ charactarestics and knowledge of artificial intelligence in obstetrics and gynaecology, their attitudes towards its introduction and perception of importance. Multivariable logistic regression analysis was used to identify predictors of low readiness.
Results:
A total of 218 trainees were recruited, median (range) for age was 28 years (24–38), 82% were females, 53.7% were senior trainees, 67.9% were currently working in public hospitals, 65.1% reported an average knowledge level about artificial intelligence in obstetrics and gynaecology, and 89.9% never received formal training on the medical applications of artificial intelligence. Moreover, 81.2% had average or less than average knowledge about information technology. In addition, 76.6% had low knowledge scores about the applications of artificial intelligence in obstetrics and gynaecology, 73.9% had negative attitude towards its introduction, and 78.9% had a negative perception of its importance. Overall, 77% were found to have low readiness. Statistically significant higher odds of low readiness were found among trainees who perceived the importance of artificial intelligence in the workplace as “moderate” (AOR = 2.36, 95% CI: 1.02–5.44, P = 0.044), and trainees with “average" understanding of the role of AI in medicine (AOR = 7.52, 95% CI: 1.96–28.86, P <.003) and trainees with "above average/excellent" understanding of the role of AI in medicine (AOR = 9.62, 95% CI: 2.48–37.29, P =<.001).
Conclusions:
Low readiness is prevalent among O&G trainees reflecting significant knowledge gaps, negative attitudes, and limited awareness, probably related to factors such as generational gaps, IT literacy and lack of formal undergraduate and postgraduate training. These highlight the need to integrate AI training into both undergraduate and postgraduate medical training programs Clinical Trial: Not applicable
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