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.

NCT Number	NCT07402668
Lead Sponsor	Rigshospitalet, Denmark Collaborators: Technical University of Denmark, The Foundation of 17.12.1981, Department of Computer Science, University of Copenhagen, Denmark, Copenhagen Academy for Medical Education and Simulation
Conditions	Preterm Birth, Artificial Intelligence (AI) in Diagnosis
Enrollment	125 participants
Start Date	2026-02-03
Primary Completion	2026-07 (estimated)
Study Completion	2026-07 (estimated)
Updated on ClinicalTrials.gov	2026-07-01