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Recruiting NCT07402668

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

No phase Interventional Preterm Birth Artificial Intelligence (AI) in Diagnosis

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: AI prediction (binary), AI risk estimate (%).
Who it may be relevant to
Registry conditions: Preterm Birth, Artificial Intelligence (AI) in Diagnosis. Basic parameters: No limits · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
Denmark
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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Overview

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.

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.

Primary outcome measures

  • Clinician diagnostic calibration (accuracy-confidence alignment) after AI exposure. [Time frame: Immediately after AI exposure during a single questionnaire session (approximately 20 minutes).]
Secondary outcome measures (4)
  • 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).]
  • 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).]
  • 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).]
  • Follow-up cervical ultrasound planning. [Time frame: Baseline (pre-AI) and immediately after AI exposure during a single questionnaire session (approximately 20 minutes).]

Eligibility 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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Allocation
Randomized
Model
Parallel assignment
Masking
Single blind
Primary purpose
Other

Study locations

Denmark · 18 centers
  • South Jutland Hospital — Aabenraa
  • Aalborg University Hospital (Aalborg and Thisted) — Aalborg
  • Aarhus University Hospital — Aarhus
  • Copenhagen University Hospital, Rigshospitalet — Copenhagen
  • Esbjerg Hospital — Esbjerg
  • Gødstrup Regional Hospital — Gødstrup
  • Herlev Hospital — Herlev
  • Copenhagen University Hospital, North Zealand — Hillerød
  • … and 10 more centers

Identifiers

NCT: NCT07402668 · P-2024-18108

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗