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

June 2, 2026 updated by: Emilie Pi Fogtmann Sejer, Rigshospitalet, Denmark

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Estimated)

125

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Other
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: AI prediction
The participants receive a binary AI prediction (preterm or term birth)
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.
Experimental: AI risk estimate
The participants receive an AI risk estimate of preterm birth (%)
Behavioral: AI risk estimate (%)
AI decision support based on cervical ultrasound providing an estimate of preterm birth risk (%) in addition to standard clinical information.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Clinician diagnostic calibration (accuracy-confidence alignment) after AI exposure.
Time Frame: Immediately after AI exposure during a single questionnaire session (approximately 20 minutes).
Agreement between post-AI decision correctness (0/1) and post-AI confidence rating (0-10) will be quantified using the Brier score. Confidence will be rescaled to 0-1 and squared differences between confidence and correctness will be averaged across cases to produce a participant-level score. Lower scores indicate better diagnostic calibration. Results will be compared between randomized arms.
Immediately after AI exposure during a single questionnaire session (approximately 20 minutes).

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Helpful switch rate and harmful switch rate.
Time Frame: Baseline (pre-AI) and immediately after AI exposure during a single questionnaire session (approximately 20 minutes).

Proportion of cases with helpful and harmful switches calculated for each participant and compared between study arms.

Helpful switch = incorrect pre-AI decision changing to correct post-AI decision.

Harmful switch = correct pre-AI decision changing to incorrect post-AI decision.
Baseline (pre-AI) and immediately after AI exposure during a single questionnaire session (approximately 20 minutes).
Change in decision accuracy, confidence, and diagnostic calibration from pre-AI to post-AI.
Time Frame: Baseline (pre-AI) and immediately after AI exposure during a single questionnaire session (approximately 20 minutes).
Within-participant change from pre-AI to post-AI in decision accuracy (proportion of correct decisions), confidence rating, and diagnostic calibration. Differences will be compared between randomized arms and stratified by AI correctness.
Baseline (pre-AI) and immediately after AI exposure during a single questionnaire session (approximately 20 minutes).
Association between self-rated trust in AI and behavioral reliance on AI.
Time Frame: Immediately after AI exposure during a single questionnaire session (approximately 20 minutes).
Self-rated trust in the AI output will be measured using a numeric rating scale (0-10) after AI exposure for each case. Behavioral reliance will be quantified as the proportion of post-AI decisions concordant with the AI output. The relationship between trust ratings and behavioral reliance, including concordance when the AI is correct and incorrect, will be evaluated at the participant level and compared between randomized arms.
Immediately after AI exposure during a single questionnaire session (approximately 20 minutes).
Follow-up cervical ultrasound planning.
Time Frame: Baseline (pre-AI) and immediately after AI exposure during a single questionnaire session (approximately 20 minutes).
Proportion of cases in which clinicians plan an additional cervical ultrasound (yes/no), summarized per participant and compared pre-post AI and between randomized arms.
Baseline (pre-AI) and immediately after AI exposure during a single questionnaire session (approximately 20 minutes).

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Rigshospitalet, Denmark

Collaborators

Technical University of Denmark
Copenhagen Academy for Medical Education and Simulation
The Foundation of 17.12.1981
Department of Computer Science, University of Copenhagen, Denmark

Investigators

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

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

February 3, 2026

Primary Completion (Estimated)

June 1, 2026

Study Completion (Estimated)

June 1, 2026

Study Registration Dates

First Submitted

January 27, 2026

First Submitted That Met QC Criteria

February 3, 2026

First Posted (Actual)

February 11, 2026

Study Record Updates

Last Update Posted (Actual)

June 3, 2026

Last Update Submitted That Met QC Criteria

June 2, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • P-2024-18108

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Individual participant data are not planned to be publicly shared. Aggregate results will be reported. De-identified data may be made available upon reasonable request and subject to institutional policies and applicable data protection regulations.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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