How different AI displays change clinicians' confidence when predicting preterm birth

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

Not applicable Interventional Rigshospitalet, Denmark · NCT07402668

This project tests whether showing AI as a yes/no prediction or as a percentage risk changes how obstetricians and trainees judge and manage the chance of spontaneous preterm birth.

Quick facts

PhaseNot applicable
Study typeInterventional
Enrollment125 (estimated)
SexAll
SponsorRigshospitalet, Denmark Academic / other
Locations7 sites (Copenhagen and 6 other locations)
Trial IDNCT07402668 on ClinicalTrials.gov

What this trial studies

In a randomized, questionnaire-based design obstetricians and trainees review clinical case vignettes that include cervical ultrasound images and make diagnostic and management decisions. Participants rate their confidence before and after seeing one of two AI outputs: a binary classification (preterm vs term) or an individualized percentage risk estimate. The study compares how each AI format affects diagnostic calibration (alignment of confidence with accuracy) and whether AI leads to helpful or harmful changes in clinical decisions. Data are collected online and analyzed to identify differences in decision changes and confidence calibration between the two presentation formats.

Who should consider this trial

Good fit: Ideal participants are medical doctors working in or training in obstetrics and gynecology who have experience performing transvaginal cervical ultrasound examinations.

Not a fit: Clinicians without transvaginal cervical ultrasound experience, non-physician staff, and pregnant patients themselves would not directly benefit from participation in this project.

Why it matters

Potential benefit: If successful, the findings could guide how AI outputs are shown so clinicians make better-calibrated decisions and avoid unnecessary interventions or missed risks.

How similar studies have performed: Prior research on AI decision support has shown mixed results—some work improves accuracy and calibration while other studies report overreliance or automation bias—so this question is partially tested but still active.

Eligibility criteria

Show full inclusion / exclusion criteria
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.

Where this trial is running

Copenhagen and 6 other locations

Study contacts

How to participate

  1. Review the eligibility criteria above with your treating physician.
  2. Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
  3. Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions Preterm BirthArtificial Intelligence in DiagnosisPreterm birthPremature birthDiagnostic calibrationDiagnostic accuracyDiagnostic confidenceArtificial intelligence
Last reviewed 2026-06-09 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.