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Explainable AI in Medical Education: CerViD-MultiModal Framework Trial

NCT07743658 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 120

Last updated 2026-08-04

No results posted yet for this study

Summary

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

Conditions

  • Medical Education

Interventions

OTHER

Traditional AI Lecture Module

Standard educational instruction delivered via traditional slides and static charts explaining neuroimaging AI outputs.

OTHER

XAI-Enhanced Interactive Module (CerViD-MultiModal)

Standard educational instruction delivered via traditional slides and static charts explaining neuroimaging AI outputs.

Sponsors & Collaborators

  • National Institute on Aging (NIA)

    collaborator NIH

  • University of Liberia

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-05-30
Primary Completion
2026-05-30
Completion
2026-06-15

Countries

  • Liberia

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07743658 on ClinicalTrials.gov