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ER-VISION-AI Study (ER-VISION-AI)

21. juli 2026 opdateret af: Ewha Womans University Mokdong Hospital

Multimodal Visual Language Model-Assisted Diagnostic Strategy in the Emergency Department: A Prospective Multicenter Randomized Controlled Trial (ER-VISION-AI Study)

Prospective, multicenter, randomized, open-label, blinded-endpoint (PROBE-like) clinical trial evaluating whether physician-supervised Generative Pre-trained Transformer (GPT)-assisted multimodal diagnostic support improves diagnostic concordance in emergency department patients presenting with acute cardiopulmonary symptoms.

Studieoversigt

Status

Ikke rekrutterer endnu

Betingelser

Intervention / Behandling

Undersøgelsestype

Interventionel

Tilmelding (Anslået)

1000

Fase

  • Ikke anvendelig

Kontakter og lokationer

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

Studiekontakt

Studiesteder

  • Sydkorea
      • Seoul, Sydkorea, 07804
        • Ewha Womans University Mokdong Hospital
        • Kontakt:

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

Ingen

Beskrivelse

Inclusion Criteria:

  • Age ≥18 years
  • Presentation to a participating emergency department with acute cardiopulmonary symptoms, including chest pain, dyspnea, palpitations, syncope, dizziness, or fever accompanied by cardiopulmonary symptoms
  • Performance of both a standard 12-lead electrocardiogram and chest radiography during the initial emergency department evaluation
  • Availability of initial clinical assessment, vital signs, laboratory findings, and all mandatory clinical information required for the multimodal AI workflow
  • Expected emergency department observation or hospital admission for at least 24 hours
  • Ability and willingness to provide written informed consent

Exclusion Criteria:

  • Inability or refusal to provide written informed consent
  • Requirement for immediate life-saving intervention that precludes completion of the study workflow
  • Death before completion of the initial emergency department diagnostic assessment
  • Electrocardiographic quality insufficient for reliable physician or Artificial intelligence (AI) interpretation
  • Chest radiographic quality insufficient for reliable physician or Artificial intelligence (AI) interpretation
  • Cardiac pacing rhythm
  • Missing mandatory clinical information required for the multimodal Artificial intelligence (AI) workflow
  • Previous enrollment in the ER-VISION-AI trial
  • Inability to establish a blinded adjudicated reference diagnosis

Studieplan

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

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

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

Våben og indgreb

Deltagergruppe / Arm
Intervention / Behandling
Eksperimentel: GPT-assisted multimodal visual language model (VLM) diagnostic strategy
Participants receive physician-supervised Generative Pre-trained Transformer (GPT)-assisted multimodal diagnostic support integrating electrocardiography, chest radiography, structured clinical information, laboratory findings, vital signs, and relevant medical history. Treating physicians remain responsible for all diagnostic and therapeutic decisions.
Diagnostisk test: Generative Pre-trained Transformer (GPT)-assisted multimodal visual language model (VLM) diagnostic support
A Generative Pre-trained Transformer (GPT)-based multimodal visual language model integrates electrocardiograms, chest radiographs, structured clinical information, laboratory findings, vital signs, and relevant clinical history to generate diagnostic suggestions and differential diagnoses for physician-supervised clinical decision support.
Aktiv komparator: Conventional physician-guided diagnostic strategy
Participants undergo standard emergency department diagnostic evaluation according to routine clinical practice without Generative Pre-trained Transformer (GPT)-assisted diagnostic support.
Diagnostisk test: Conventional emergency department diagnostic evaluation
Routine emergency department diagnostic evaluation performed according to standard clinical practice without AI-assisted diagnostic support.

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Diagnostic concordance between the final emergency department diagnosis and the blinded adjudicated reference diagnosis established at hospital discharge.
Tidsramme: During the index hospitalization, up to hospital discharge (average 3 days)
Diagnostic concordance between the treating physician's final emergency department diagnosis and the blinded adjudicated reference diagnosis based on the prespecified principal diagnostic category.
During the index hospitalization, up to hospital discharge (average 3 days)

Sekundære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Diagnostic concordance after Generative Pre-trained Transformer (GPT)-assisted diagnostic support
Tidsramme: During the index emergency department visit (average 6 hours)
Diagnostic concordance between the physician's final emergency department diagnosis after Generative Pre-trained Transformer (GPT)-assisted multimodal diagnostic support and the blinded adjudicated reference diagnosis in participants assigned to the intervention group.
During the index emergency department visit (average 6 hours)
Time from emergency department presentation to final diagnosis
Tidsramme: During the index emergency department visit (average 6 hours)
Time required from emergency department presentation until establishment of the physician's final emergency department diagnosis.
During the index emergency department visit (average 6 hours)
Diagnostic reclassification after Generative Pre-trained Transformer (GPT)-assisted evaluation
Tidsramme: During the index emergency department visit (average 6 hours)
Frequency of changes between the physician's initial working diagnosis and the final emergency department diagnosis after review of Generative Pre-trained Transformer (GPT)-generated diagnostic recommendations.
During the index emergency department visit (average 6 hours)
Physician diagnostic confidence
Tidsramme: During the index emergency department visit (average 6 hours)
Physician-reported diagnostic confidence recorded before and after Generative Pre-trained Transformer (GPT)-assisted diagnostic support using the prespecified study assessment scale.
During the index emergency department visit (average 6 hours)
Physician acceptance of Generative Pre-trained Transformer (GPT)-generated diagnostic recommendations
Tidsramme: During the index emergency department visit (average 6 hours)
Frequency of physician acceptance, modification, or rejection of Generative Pre-trained Transformer (GPT)-generated diagnostic recommendations in the intervention group.
During the index emergency department visit (average 6 hours)
Emergency department disposition accuracy
Tidsramme: Up to hospital discharge (average 3 days)
Accuracy of emergency department disposition decisions, including discharge, hospital admission, or intensive care unit admission, compared with the adjudicated reference diagnosis.
Up to hospital discharge (average 3 days)
Emergency department length of stay
Tidsramme: Up to hospital discharge (average 3 days)
Length of stay in the emergency department measured from patient presentation until emergency department discharge or hospital admission.
Up to hospital discharge (average 3 days)
Hospital length of stay
Tidsramme: Up to hospital discharge (average 3 days)
Total duration of hospitalization from admission until hospital discharge.
Up to hospital discharge (average 3 days)
In-hospital mortality
Tidsramme: Up to hospital discharge (average 3 days)
All-cause mortality occurring during the index hospitalization.
Up to hospital discharge (average 3 days)
30-day all-cause mortality
Tidsramme: 30 days
All-cause mortality occurring within 30 days after the index emergency department visit.
30 days
30-day emergency department revisit
Tidsramme: 30 days
Revisit to any emergency department for any cause within 30 days after the index emergency department visit.
30 days
30-day hospital readmission
Tidsramme: 30 days
Hospital readmission for any cause within 30 days after discharge from the index hospitalization.
30 days

Samarbejdspartnere og efterforskere

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

Sponsor

Ewha Womans University Mokdong Hospital

Samarbejdspartnere

Ewha Womans University Seoul Hospital

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

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 (Anslået)

1. januar 2027

Primær færdiggørelse (Anslået)

31. december 2028

Studieafslutning (Anslået)

31. december 2029

Datoer for studieregistrering

Først indsendt

18. juli 2026

Først indsendt, der opfyldte QC-kriterier

21. juli 2026

Først opslået (Faktiske)

27. juli 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

27. juli 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

21. juli 2026

Sidst verificeret

1. juli 2026

Mere information

Begreber relateret til denne undersøgelse

Nøgleord

Yderligere relevante MeSH-vilkår

Andre undersøgelses-id-numre

  • ER-VISION-AI study

Plan for individuelle deltagerdata (IPD)

Planlægger du at dele individuelle deltagerdata (IPD)?

UBESLUTET

IPD-planbeskrivelse

The investigators have not yet determined whether de-identified individual participant data (IPD), including the analyzable dataset and supporting documentation, will be shared with researchers outside the study team. A final decision will be made after completion of the study, taking into consideration institutional policies, participant privacy, ethical requirements, and applicable regulations.

Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter

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