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

21. juli 2026 oppdatert av: 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.

Studieoversikt

Status

Har ikke rekruttert ennå

Forhold

Intervensjon / Behandling

Studietype

Intervensjonell

Registrering (Antatt)

1000

Fase

  • Ikke aktuelt

Kontakter og plasseringer

Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.

Studiekontakt

Studiesteder

  • Sør -Korea
      • Seoul, Sør -Korea, 07804
        • Ewha Womans University Mokdong Hospital
        • Ta kontakt med:

Deltakelseskriterier

Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.

Kvalifikasjonskriterier

Alder som er kvalifisert for studier

  • Voksen
  • Eldre voksen

Tar imot friske frivillige

Nei

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

Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.

Hvordan er studiet utformet?

Designdetaljer

  • Primært formål: Diagnostisk
  • Tildeling: Randomisert
  • Intervensjonsmodell: Parallell tildeling
  • Masking: Ingen (Open Label)

Våpen og intervensjoner

Deltakergruppe / Arm
Intervensjon / Behandling
Eksperimentell: 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.

Hva måler studien?

Primære resultatmål

Resultatmål
Tiltaksbeskrivelse
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
Tiltaksbeskrivelse
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

Samarbeidspartnere og etterforskere

Det er her du vil finne personer og organisasjoner som er involvert i denne studien.

Sponsor

Ewha Womans University Mokdong Hospital

Samarbeidspartnere

Ewha Womans University Seoul Hospital

Publikasjoner og nyttige lenker

Den som er ansvarlig for å legge inn informasjon om studien leverer frivillig disse publikasjonene. Disse kan handle om alt relatert til studiet.

Generelle publikasjoner

Studierekorddatoer

Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.

Studer hoveddatoer

Studiestart (Antatt)

1. januar 2027

Primær fullføring (Antatt)

31. desember 2028

Studiet fullført (Antatt)

31. desember 2029

Datoer for studieregistrering

Først innsendt

18. juli 2026

Først innsendt som oppfylte QC-kriteriene

21. juli 2026

Først lagt ut (Faktiske)

27. juli 2026

Oppdateringer av studieposter

Sist oppdatering lagt ut (Faktiske)

27. juli 2026

Siste oppdatering sendt inn som oppfylte QC-kriteriene

21. juli 2026

Sist bekreftet

1. juli 2026

Mer informasjon

Begreper knyttet til denne studien

Nøkkelord

Ytterligere relevante MeSH-vilkår

Andre studie-ID-numre

  • ER-VISION-AI study

Plan for individuelle deltakerdata (IPD)

Planlegger du å dele individuelle deltakerdata (IPD)?

UBESLUTTE

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.

Legemiddel- og utstyrsinformasjon, studiedokumenter

Studerer et amerikansk FDA-regulert medikamentprodukt

Nei

Studerer et amerikansk FDA-regulert enhetsprodukt

Nei

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