Clinicians' Trust in AI-Based Fetal Growth Estimates

Clinicians' Trust and Decision-Making Using AI-Based Fetal Growth Estimates With and Without Uncertainty: A Randomized Questionnaire Study

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Pregnancy; Clinical Decision-making; Fetal Growth; Obstetric Ultrasonography
• Intervention / Treatment: Other: Intervention - AI Performance Information

Detailed Description

This is a randomized, matched, vignette-based questionnaire study designed to investigate clinicians' trust in and use of AI-based fetal growth estimates.

Clinicians from obstetrics and gynecology departments will be recruited and stratified by experience level. Participants will be randomized to either a control group or an intervention group. The intervention group will receive brief information about the AI model's overall performance, while the control group will not receive this information.

Each participant will assess a set of anonymized third-trimester ultrasound cases. For each case, clinicians will be presented with standard ultrasound images and relevant clinical context. They will be shown fetal weight estimates generated by an AI-based model and by a traditional biometric method, with or without accompanying uncertainty information in the form of confidence intervals.

For each case, clinicians will select the estimate they consider most clinically reliable, rate their confidence in that choice, and indicate whether they would recommend a follow-up growth scan. Case sets are matched by clinical experience, ensuring that identical cases are evaluated by clinicians with similar backgrounds across study arms.

The study focuses on clinicians as participants and involves no patient intervention. All ultrasound data are fully anonymized. The results will provide insight into how AI-generated estimates and uncertainty information influence clinical trust, preferences, and decision-making in fetal growth assessment.

• Study Type: Interventional
• Enrollment (Estimated): 308
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Zahra Bashir, MD; Phone Number: 004574871407; Email: zab@regsj.dk

Study Locations

• Denmark
  • Slagelse, Denmark, 4200
    • Department of Obstetrics and Gynecology, Slagelse Hospital
    • Contact: Zahra Bashir, MD; Phone Number: +45 58 55 37 05; Email: zab@regsj.dk

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

• Clinicians working in obstetrics and gynecology departments.
• Regular use of obstetric ultrasound in clinical practice.
• Willingness to participate in a questionnaire-based study.

Exclusion Criteria:

• Clinicians who do not perform obstetric ultrasound examinations.
• Clinicians with a known conflict of interest related to the AI system being evaluated.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
No Intervention: Control - No AI Performance Information; Participants complete the questionnaire without receiving information about the AI model's overall performance.
Other: ntervention - AI Performance Information; Participants receive brief information about the AI model's overall performance before completing the questionnaire.	Other: Intervention - AI Performance Information; Participants receive brief information about the AI model's overall performance before completing the questionnaire.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Clinicians' choice of fetal weight estimation method	The proportion of cases in which clinicians choose the AI-based fetal weight estimate rather than the traditional Hadlock estimate when assessing anonymized ultrasound cases.	Immediately after questionnaire completion

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Clinicians' confidence in selected fetal weight estimate	Clinicians' self-reported confidence in the selected fetal weight estimate, measured on a 7-point Likert scale for each case.	Immediately after questionnaire completion
Recommendation of follow-up growth scan	Whether clinicians recommend a follow-up fetal growth scan based on the selected fetal weight estimate, recorded as a binary outcome (yes/no).	Immediately after questionnaire completion
Impact of uncertainty information on model preference	Difference in clinicians' preference for AI-based versus traditional fetal weight estimates when AI predictions are presented with versus without uncertainty information.	Immediately after questionnaire completion

Collaborators and Investigators

• Sponsor: Rigshospitalet, Denmark
• Collaborators: Slagelse Hospital; Copenhagen Academy for Medical Education and Simulation

Study record dates

Study Major Dates

• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 1, 2028

Study Registration Dates

• First Submitted: January 23, 2026
• First Submitted That Met QC Criteria: February 3, 2026
• First Posted (Actual): February 10, 2026

Study Record Updates

• Last Update Posted (Actual): February 10, 2026
• Last Update Submitted That Met QC Criteria: February 3, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Keywords: Artificial intelligence; Clinical decision-making; Trust; Fetal weight estimation; Obstetric ultrasound; Human-AI interaction; Questionnaire study
• Other Study ID Numbers: F-25022462

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No