NCT07402668

Brief 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.

Trial Health

57
Monitor

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
125

participants targeted

Target at P50-P75 for not_applicable

Timeline
Completed

Started Feb 2026

Shorter than P25 for not_applicable

Geographic Reach
1 country

7 active sites

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

January 27, 2026

Completed
7 days until next milestone

Study Start

First participant enrolled

February 3, 2026

Completed
8 days until next milestone

First Posted

Study publicly available on registry

February 11, 2026

Completed
4 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

June 1, 2026

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

June 1, 2026

Completed
Last Updated

June 3, 2026

Status Verified

May 1, 2026

Enrollment Period

4 months

First QC Date

January 27, 2026

Last Update Submit

June 2, 2026

Conditions

Preterm BirthArtificial Intelligence (AI) in Diagnosis

Keywords

Preterm birthPremature birthDiagnostic calibrationDiagnostic accuracyDiagnostic confidenceArtificial intelligence

Outcome Measures

Primary Outcomes (1)

  • Clinician diagnostic calibration (accuracy-confidence alignment) after AI exposure.

    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 Outcomes (4)

  • Helpful switch rate and harmful switch rate.

    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.

    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.

    Immediately after AI exposure during a single questionnaire session (approximately 20 minutes).

  • Follow-up cervical ultrasound planning.

    Baseline (pre-AI) and immediately after AI exposure during a single questionnaire session (approximately 20 minutes).

Study Arms (2)

AI prediction

EXPERIMENTAL

The participants receive a binary AI prediction (preterm or term birth)

Behavioral: AI prediction (binary)

AI risk estimate

EXPERIMENTAL

The participants receive an AI risk estimate of preterm birth (%)

Behavioral: AI risk estimate (%)

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 prediction
AI risk estimate (%)BEHAVIORAL

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

AI risk estimate

Eligibility Criteria

Sexall
Healthy VolunteersYes
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

  • Medical doctors currently working in or training within the field of obstetrics and gynecology.
  • Experience performing transvaginal cervical ultrasound examinations.

You may not qualify if:

  • \- No prior experience performing transvaginal cervical ultrasound examinations.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (7)

Copenhagen University Hospital, Rigshospitalet

Copenhagen, 2100, Denmark

RECRUITING

Herlev Hospital

Herlev, 2730, Denmark

RECRUITING

Copenhagen University Hospital, North Zealand

Hillerød, 3400, Denmark

RECRUITING

Holbæk Hospital

Holbæk, 4300, Denmark

RECRUITING

Hvidovre Hospital

Hvidovre, 2650, Denmark

RECRUITING

Zealand University Hospital, Roskilde

Roskilde, 4000, Denmark

RECRUITING

Slagelse Hospital

Slagelse, 4200, Denmark

RECRUITING

MeSH Terms

Conditions

Premature Birth

Condition Hierarchy (Ancestors)

Obstetric Labor, PrematureObstetric Labor ComplicationsPregnancy ComplicationsFemale Urogenital Diseases and Pregnancy ComplicationsUrogenital Diseases

Study Officials

  • Martin G Tolsgaard, MD, PhD, DMSc

    Department of Obstetrics and Gynecology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark

    STUDY CHAIR

Central Study Contacts

Emilie Pi F Sejer, MD

CONTACT

0045 28890690emilie.pi.fogtmann.sejer.01@regionh.dk

Study Design

Study Type
interventional
Phase
not applicable
Allocation
RANDOMIZED
Masking
SINGLE
Who Masked
PARTICIPANT
Masking Details
Participants (clinicians) are blinded to randomized allocation and are unaware that different versions of the AI decision support are being compared. They view only the AI output presented within their assigned condition. No independent outcome assessors are involved. Outcomes are derived using pre-specified, objective scoring rules.
Purpose
OTHER
Intervention Model
PARALLEL
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Principal Investigator

Study Record Dates

First Submitted

January 27, 2026

First Posted

February 11, 2026

Study Start

February 3, 2026

Primary Completion

June 1, 2026

Study Completion

June 1, 2026

Last Updated

June 3, 2026

Record last verified: 2026-05

Data Sharing

IPD Sharing
Will not share

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.

Locations

DK(7)