Clinicians' Trust and Decision-Making Using AI-Based Fetal Growth Estimates With and Without Uncertainty: A Randomized Questionnaire Study
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 入组人数
- 130
- 试验地点
- 1
- 主要终点
- Clinicians' choice of fetal weight estimation method
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Health Services Research
- 盲法
- Single (Participant)
盲法说明
Participants are unaware of their allocation to the control or intervention group and are not informed that different versions of the questionnaire exist.
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Clinicians working in obstetrics and gynecology departments.
- •Regular use of obstetric ultrasound in clinical practice.
- •Willingness to participate in a questionnaire-based study.
排除标准
- •Clinicians who do not perform obstetric ultrasound examinations.
- •Clinicians with a known conflict of interest related to the AI system being evaluated.
研究组 & 干预措施
ntervention - AI Performance Information
Participants receive brief information about the AI model's overall performance before completing the questionnaire.
干预措施: Intervention - AI Performance Information (Other)
Control - No AI Performance Information
Participants complete the questionnaire without receiving information about the AI model's overall performance.
结局指标
主要结局
Clinicians' choice of fetal weight estimation method
时间窗: 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.
次要结局
- Clinicians' confidence in selected fetal weight estimate(Immediately after questionnaire completion)
- Recommendation of follow-up growth scan(Immediately after questionnaire completion)
- Impact of uncertainty information on model preference(Immediately after questionnaire completion)
研究者
Zahra Bashir
Dr.
Rigshospitalet, Denmark