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

Sponsor
Rigshospitalet, Denmark
Study ID
NCT07402668
Status
Recruiting
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Conditions

  • Artificial Intelligence (AI) in Diagnosis
  • Preterm Birth

Eligibility Criteria

Sex
ALL
Age
N/A - N/A
Healthy Volunteers
Accepted

Interventions

  • AI prediction (binary) — BEHAVIORAL
    AI decision support based on cervical ultrasound providing a binary classification (preterm birth before 37 weeks or term birth) in addition to standard clinical information.
  • AI risk estimate (%) — BEHAVIORAL
    AI decision support based on cervical ultrasound providing an estimate of preterm birth risk (%) in addition to standard clinical information.

Study Details

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.

Key Dates

First listed
Feb 11, 2026
Start date
Feb 3, 2026
Status verified
Jun 2026
Primary completion
Jul 31, 2026
Completion
Jul 31, 2026

Study Design

Enrollment
125 participants (estimated)
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER

Arms

  • Experimental: AI prediction
    The participants receive a binary AI prediction (preterm or term birth)
  • Experimental: AI risk estimate
    The participants receive an AI risk estimate of preterm birth (%)

Primary Outcome Measure

Clinician diagnostic calibration (accuracy-confidence alignment) after AI exposure. [ Time Frame: Immediately after AI exposure during a single questionnaire session (approximately 20 minutes). ]

Central Contacts

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