Clinicians' Trust in AI-Based Fetal Growth Estimates

This study examines how clinicians trust and use artificial intelligence (AI) when estimating fetal weight during pregnancy. Accurate assessment of fetal growth is important for identifying growth problems that may affect pregnancy management. New AI-based tools can estimate fetal weight from ultrasound images, but little is known about how clinicians trust these estimates or how uncertainty information influences their decisions. In this study, clinicians will review anonymized ultrasound cases and compare fetal weight estimates generated by an AI model with traditional estimates. Some clinicians will also be shown information about the AI model's performance and uncertainty, while others will not. Participants will be asked to choose which estimate they find most reliable, indicate their level of confidence, and decide whether they would recommend follow-up scans. The study aims to better understand how AI and uncertainty information affect clinical decision-making and trust among clinicians with different levels of experience.

Inclusion Criteria: * Clinicians working in obstetrics and gynecology departments. * Regular use of obstetric ultrasound in clinical practice. * Willingness to participate in a questionnaire-based study. Exclusion Criteria: * Clinicians who do not perform obstetric ultrasound examinations. * Clinicians with a known conflict of interest related to the AI system being evaluated.