Clinicians' Trust in AI-Based Fetal Growth Estimates
- Sponsor
- Rigshospitalet, Denmark
- Study ID
- NCT07401368
- Status
- Not Yet Recruiting
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Conditions
- Clinical Decision-making
- Fetal Growth
- Obstetric Ultrasonography
- Pregnancy
Eligibility Criteria
- Sex
- ALL
- Age
- N/A - N/A
- Healthy Volunteers
- Accepted
Interventions
- Intervention - AI Performance Information — OTHERParticipants receive brief information about the AI model's overall performance before completing the questionnaire.
Study Details
This study examines how clinicians trust and use artificial intelligence (AI) when estimating fetal weight during pregnancy. Accurate assessment of fetal growth is important for identifying growth problems that may affect pregnancy management. New AI-based tools can estimate fetal weight from ultrasound images, but little is known about how clinicians trust these estimates or how uncertainty information influences their decisions. In this study, clinicians will review anonymized ultrasound cases and compare fetal weight estimates generated by an AI model with traditional estimates. Some clinicians will also be shown information about the AI model's performance and uncertainty, while others will not. Participants will be asked to choose which estimate they find most reliable, indicate their level of confidence, and decide whether they would recommend follow-up scans. The study aims to better understand how AI and uncertainty information affect clinical decision-making and trust among clinicians with different levels of experience.
Key Dates
- First listed
- Feb 10, 2026
- Start date
- Jun 1, 2026
- Status verified
- Feb 2026
- Primary completion
- Dec 1, 2027
- Completion
- Dec 1, 2028
Study Design
- Enrollment
- 308 participants (estimated)
- Allocation
- RANDOMIZED
- Intervention model
- PARALLEL
- Primary purpose
- HEALTH_SERVICES_RESEARCH
Arms
- No Intervention: Control - No AI Performance InformationParticipants complete the questionnaire without receiving information about the AI model's overall performance.
- Other: ntervention - AI Performance InformationParticipants receive brief information about the AI model's overall performance before completing the questionnaire.
Primary Outcome Measure
Clinicians' choice of fetal weight estimation method [ Time Frame: Immediately after questionnaire completion ]
Central Contacts
- Zahra Bashir, MD004574871407
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