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

29. juli 2026 opdateret af: 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

Studieoversigt

Status

Afsluttet

Betingelser

Intervention / Behandling

Detaljeret beskrivelse

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)

Undersøgelsestype

Interventionel

Tilmelding (Faktiske)

120

Fase

  • Ikke anvendelig

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiesteder

  • Liberia
    • Montserrado County
      • Monrovia, Montserrado County, Liberia, 1000
        • University of Liberia Medical School

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

  • Voksen
  • Ældre voksen

Tager imod sunde frivillige

Ja

Beskrivelse

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.

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

  • Primært formål: Sundhedstjenesteforskning
  • Tildeling: Randomiseret
  • Interventionel model: Parallel tildeling
  • Maskning: Ingen (Åben etiket)

Våben og indgreb

Deltagergruppe / Arm
Intervention / Behandling
Aktiv komparator: Control Group
Participants complete a 45-minute traditional lecture module on AI in neuroimaging using static text and bar charts
Andet: Traditional AI Lecture Module
Standard educational instruction delivered via traditional slides and static charts explaining neuroimaging AI outputs.
Eksperimentel: 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
Andet: XAI-Enhanced Interactive Module (CerViD-MultiModal)
Standard educational instruction delivered via traditional slides and static charts explaining neuroimaging AI outputs.
Andre navne:
  • CerViD-MultiModal Framework
  • SHAP and LIME Educational Module

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
AI Literacy Score
Tidsramme: 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
Tidsramme: 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)

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

University of Liberia

Samarbejdspartnere

National Institute on Aging (NIA)

Publikationer og nyttige links

Den person, der er ansvarlig for at indtaste oplysninger om undersøgelsen, leverer frivilligt disse publikationer. Disse kan handle om alt relateret til undersøgelsen.

Generelle publikationer

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

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Faktiske)

30. maj 2026

Primær færdiggørelse (Faktiske)

30. maj 2026

Studieafslutning (Faktiske)

15. juni 2026

Datoer for studieregistrering

Først indsendt

29. juli 2026

Først indsendt, der opfyldte QC-kriterier

29. juli 2026

Først opslået (Faktiske)

4. august 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

4. august 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

29. juli 2026

Sidst verificeret

1. juli 2026

Mere information

Begreber relateret til denne undersøgelse

Nøgleord

Yderligere relevante MeSH-vilkår

Andre undersøgelses-id-numre

  • UL-IRB-2026-XAI-MED
  • U01AG024904 (U.S. NIH-bevilling/kontrakt)

Plan for individuelle deltagerdata (IPD)

Planlægger du at dele individuelle deltagerdata (IPD)?

JA

IPD-planbeskrivelse

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-delingstidsramme

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-delingsadgangskriterier

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-deling Understøttende informationstype

  • STUDY_PROTOCOL
  • SAP
  • ANALYTIC_CODE

Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter

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Disse oplysninger blev hentet direkte fra webstedet clinicaltrials.gov uden ændringer. Hvis du har nogen anmodninger om at ændre, fjerne eller opdatere dine undersøgelsesoplysninger, bedes du kontakte register@clinicaltrials.gov. Så snart en ændring er implementeret på clinicaltrials.gov, vil denne også blive opdateret automatisk på vores hjemmeside .

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