Does AI Make Clinicians More Appropriately Confident? A Randomized Study in Preterm Birth Prediction

The goal of this randomized questionnaire-based study is to evaluate how different presentations of artificial intelligence (AI) decision support influence clinical judgment among medical doctors working in obstetrics and gynecology when assessing the risk of spontaneous preterm birth using clinical case vignettes with cervical ultrasound images. The study specifically compares two AI presentation formats: a binary classification (preterm vs term birth) and an individualized risk estimate of preterm birth. The main questions it aims to answer are: * Which AI presentation format leads to better alignment between clinicians' confidence and decision accuracy (diagnostic calibration)? * Do different AI presentation formats lead to helpful or harmful changes in clinical decisions? Participants will complete an online questionnaire in which they review clinical cases, make diagnostic and management decisions, rate their diagnostic confidence before and after seeing the AI output, and report their trust in the AI.

Inclusion Criteria: * Medical doctors currently working in or training within the field of obstetrics and gynecology. * Experience performing transvaginal cervical ultrasound examinations. Exclusion Criteria: \- No prior experience performing transvaginal cervical ultrasound examinations.