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Explainable AI in Medical Education: CerViD-MultiModal Framework Trial (CerViD-MM)

2026年7月29日 更新者：Prince L. Fully、University of Liberia

Explainable Artificial Intelligence (XAI) in Medical Education: A Multi-Modal Framework for Enhancing Human-AI Collaboration

This study evaluates whether Explainable Artificial Intelligence (XAI) explanations integrated into medical training improve AI literacy, reduce cognitive workload, and enhance learner trust compared to traditional lecture methods. Third-year medical students participated in a randomized controlled trial assessing the CerViD-MultiModal diagnostic framework during a neuroimaging diagnostic module focused on fornix atrophy in early and late mild cognitive impairment

研究概览

地位

完全的

条件

干预/治疗

详细说明

The study utilized a two-phase sequential explanatory design with mixed methodologies. In Phase 1 (Technical Development), the CerViD-MultiModal model was developed and validated using neuroimaging data from 100 Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects to classify early vs. late mild cognitive impairment via fornix morphometry features. In Phase 2 (Educational Intervention), a randomized controlled trial was conducted with 120 third-year medical students enrolled in the clinical neuroscience rotation at the University of Liberia. Participants were randomized into two equal groups (n=60 per group): Control Group: Completed a 45-minute traditional lecture module using static text and bar charts. XAI-Enhanced Group: Completed an interactive 45-minute module featuring SHAP summary charts, LIME patient-specific explanations, and interactive force graphs. Post-intervention electronic assessments evaluated four primary outcomes: AI Literacy Score (0-100 scale), System Usability Scale (SUS, 0-100 scale), perceived cognitive workload using the NASA Task Load Index (NASA-TLX, 0-100 scale), and Confidence in AI Interpretation (1-5 scale)

研究类型

介入性

注册 (实际的)

120

阶段

  • 不适用

联系人和位置

本节提供了进行研究的人员的详细联系信息，以及有关进行该研究的地点的信息。

学习地点

  • 利比里亚
    • Montserrado County
      • Monrovia、Montserrado County、利比里亚、1000
        • University of Liberia Medical School

参与标准

研究人员寻找符合特定描述的人，称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。

资格标准

适合学习的年龄

  • 成人
  • 年长者

接受健康志愿者

是的

描述

Inclusion Criteria:

  1. Enrolled as a third-year medical student in the clinical neuroscience rotation at the University of Liberia.
  2. Willing and able to complete the 45-minute educational module and post-intervention evaluations.
  3. Provided informed consent to participate in the study.

Exclusion Criteria:

  1. Prior formal coursework, professional training, or specialized technical degree in artificial intelligence, machine learning, or computer science.
  2. Inability to complete the post-intervention assessment.

学习计划

本节提供研究计划的详细信息，包括研究的设计方式和研究的衡量标准。

研究是如何设计的？

设计细节

  • 主要用途：卫生服务研究
  • 分配：随机化
  • 介入模型：并行分配
  • 屏蔽：无（打开标签）

武器和干预

参与者组/臂
干预/治疗
有源比较器：Control Group
Participants complete a 45-minute traditional lecture module on AI in neuroimaging using static text and bar charts
其他：Traditional AI Lecture Module
Standard educational instruction delivered via traditional slides and static charts explaining neuroimaging AI outputs.
实验性的：XAI-Enhanced Group
Participants complete an interactive 45-minute lecture module supplemented with CerViD-MultiModal visual XAI explanations (SHAP summary charts and LIME patient-specific explanations
其他：XAI-Enhanced Interactive Module (CerViD-MultiModal)
Standard educational instruction delivered via traditional slides and static charts explaining neuroimaging AI outputs.
其他名称：
  • CerViD-MultiModal Framework
  • SHAP and LIME Educational Module

研究衡量的是什么？

主要结果指标

结果测量
措施说明
大体时间
AI Literacy Score
大体时间：Immediately post-intervention (Day 1)
Continuous score (0-100 scale) measuring conceptual knowledge, practical application, ethical awareness, and critical evaluation of AI systems in medicine
Immediately post-intervention (Day 1)
System Usability Scale (SUS) Score
大体时间：Immediately post-intervention (Day 1)
Standardized 10-item scale assessing user perception of system usability, converted to a 0-100 overall score
Immediately post-intervention (Day 1)

合作者和调查者

在这里您可以找到参与这项研究的人员和组织。

赞助

University of Liberia

合作者

National Institute on Aging (NIA)

出版物和有用的链接

负责输入研究信息的人员自愿提供这些出版物。这些可能与研究有关。

一般刊物

  • Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems (NeurIPS). 2017;30:4765-4774.

研究记录日期

这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查，以确保它们在发布到公共网站之前符合特定的质量控制标准。

研究主要日期

学习开始 (实际的)

2026年5月30日

初级完成 (实际的)

2026年5月30日

研究完成 (实际的)

2026年6月15日

研究注册日期

首次提交

2026年7月29日

首先提交符合 QC 标准的

2026年7月29日

首次发布 (实际的)

2026年8月4日

研究记录更新

最后更新发布 (实际的)

2026年8月4日

上次提交的符合 QC 标准的更新

2026年7月29日

最后验证

2026年7月1日

更多信息

与本研究相关的术语

关键字

其他相关的 MeSH 术语

其他研究编号

  • UL-IRB-2026-XAI-MED
  • U01AG024904 (美国 NIH 拨款/合同)

计划个人参与者数据 (IPD)

计划共享个人参与者数据 (IPD)？

是的

IPD 计划说明

De-identified individual participant data collected during the study, including post-intervention assessment scores for AI literacy, System Usability Scale (SUS) ratings, NASA Task Load Index (NASA-TLX) workload metrics, and confidence scores, will be made available upon reasonable request. All direct and indirect identifiers will be removed prior to data sharing to preserve participant privacy.

IPD 共享时间框架

De-identified individual participant data and supporting documents will become available within 6 months following publication of the study results in a peer-reviewed journal and will remain accessible for up to 3 years.

IPD 共享访问标准

Data and supporting materials will be shared with qualified academic researchers and clinical educators whose formal proposal has been approved by the research team. Access is granted solely for scientific research and meta-analytic purposes. Requests should be submitted via email directly to the Principal Investigator.

IPD 共享支持信息类型

  • 研究方案
  • 树液
  • 分析代码

药物和器械信息、研究文件

研究美国 FDA 监管的药品

不

研究美国 FDA 监管的设备产品

不

此信息直接从 clinicaltrials.gov 网站检索，没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求，请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改，我们的网站上也会自动更新.

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