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

Summary

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.

Conditions

• Preterm Birth
• Artificial Intelligence (AI) in Diagnosis

Interventions

BEHAVIORAL

AI prediction (binary)

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.

BEHAVIORAL

AI risk estimate (%)

AI decision support based on cervical ultrasound providing an estimate of preterm birth risk (%) in addition to standard clinical information.

Sponsors & Collaborators

• Technical University of Denmark

  collaborator OTHER

• The Foundation of 17.12.1981

  collaborator OTHER

• Department of Computer Science, University of Copenhagen, Denmark

  collaborator UNKNOWN

• Copenhagen Academy for Medical Education and Simulation

  collaborator OTHER

• Rigshospitalet, Denmark

  lead OTHER

Principal Investigators

• Martin G Tolsgaard, MD, PhD, DMSc · Department of Obstetrics and Gynecology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark

Study Design

Allocation

RANDOMIZED

Purpose

OTHER

Masking

SINGLE

Model

PARALLEL

Eligibility

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2026-02-03

Primary Completion

2026-06-30

Completion

2026-06-30

Countries

• Denmark

Study Locations