Clinicians' Trust in AI-Based Fetal Growth Estimates

Condition(s) studied

Investigational drug(s) / intervention(s)

Intervention - AI Performance Information: Participants receive brief information about the AI model's overall performance before completing the questionnaire.

Study summary

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.

Eligibility

Primary outcome measure(s)

• Clinicians' choice of fetal weight estimation method — 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.

Trial sites (1)

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