Perspectives of Gynecologic Oncologists on AI for Women’s Cancers in Low-Resource Settings: A Survey in Iran

Artificial intelligence (AI) holds promise for improving gynecologic cancer care in low-resource settings, yet real-world adoption remains limited. This study explores the perspectives of gynecologic oncologists in Iran regarding AI’s potential, challenges, and readiness for integration into clinical practice. A cross-sectional, web-based survey was conducted among ۱۲۴ gynecologic oncologists, fellows, and senior residents in Tehran, Isfahan, and Shiraz from February to April ۲۰۲۵. The questionnaire assessed prior AI exposure, perceived knowledge, attitudes, trust, adoption barriers, and intent to use AI. Statistical analysis included descriptive statistics, Cronbach’s alpha for reliability, and multivariable logistic regression to identify predictors of adoption intent. Only ۳۹.۵% of participants had prior exposure to AI tools, primarily for imaging interpretation (۲۸.۲%) and digital pathology (۲۱.۸%). Formal AI training was rare (۱۷.۷%). Despite limited familiarity, respondents expressed cautious optimism: ۷۶.۶% agreed AI could improve diagnostic accuracy, and ۸۳.۱% supported AI as a physician adjunct rather than a replacement. Key barriers included infrastructure costs (۸۲.۳%) and concerns about bias/privacy (۷۴.۲%). High-priority applications included cervical cancer screening (۷۸.۲%) and imaging support (۷۰.۲%). In multivariable analysis, positive attitudes (aOR = ۱.۴۲, p < ۰.۰۰۱) and prior training (aOR = ۲.۸۵, p = ۰.۰۲۸) predicted higher adoption intent. Gynecologic oncologists in Iran recognize AI’s potential to address disparities in women’s cancer care but face significant infrastructural and training gaps. Targeted interventions—such as task-specific AI tools, structured training, and national guidelines—are needed to facilitate equitable implementation. These findings align with global oncology trends while highlighting context-specific challenges in low-resource settings.

Keywords:

Artificial intelligence , Gynecologic Oncology , Low-Resource Settings , Medical Surveys , Healthcare disparities