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NCT Number: NCT07401368

Clinicians' Trust in AI-Based Fetal Growth Estimates

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.

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Key information

Conditions

Fetal Growth Clinical Decision-making Obstetric Ultrasonography Pregnancy

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Intervention - AI Performance Information

Other

Participants receive brief information about the AI model's overall performance before completing the questionnaire.

Primary outcomes

  1. Clinicians' choice of fetal weight estimation method

    Time frame: Immediately after questionnaire completion

    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.

Secondary outcomes

  1. Clinicians' confidence in selected fetal weight estimate

    Time frame: Immediately after questionnaire completion

    Clinicians' self-reported confidence in the selected fetal weight estimate, measured on a 7-point Likert scale for each case.

  2. Recommendation of follow-up growth scan

    Time frame: Immediately after questionnaire completion

    Whether clinicians recommend a follow-up fetal growth scan based on the selected fetal weight estimate, recorded as a binary outcome (yes/no).

  3. Impact of uncertainty information on model preference

    Time frame: Immediately after questionnaire completion

    Difference in clinicians' preference for AI-based versus traditional fetal weight estimates when AI predictions are presented with versus without uncertainty information.

Study contacts

Contact information is provided by the study sponsor or research team.

Zahra Bashir, MD

CONTACT

[email protected]

004574871407

Sponsors and collaborators

Lead sponsor

Rigshospitalet, Denmark

Other

Collaborators

  • Copenhagen Academy for Medical Education and Simulation
  • Slagelse Hospital

Registry information

Official study title

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

Important dates

Study start
2026
Primary completion
2027
Study completion
2028
First posted
Feb 10, 2026
Registry last updated
Feb 10, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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