ER-VISION-AI Study (ER-VISION-AI)
21 juillet 2026 mis à jour par: 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.
Aperçu de l'étude
Statut
Pas encore de recrutement
Les conditions
Type d'étude
Interventionnel
Inscription (Estimé)
1000
Phase
- N'est pas applicable
Contacts et emplacements
Cette section fournit les coordonnées de ceux qui mènent l'étude et des informations sur le lieu où cette étude est menée.
Coordonnées de l'étude
- Nom: Yeji Kim, PhD
- Numéro de téléphone: +82-10-2724-7740
- E-mail: lexie6169@gmail.com
Lieux d'étude
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Seoul, Corée du Sud, 07804
- Ewha Womans University Mokdong Hospital
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Contact:
- Yeji Kim, PhD
- Numéro de téléphone: +82-10-2724-7740
- E-mail: lexie6169@gmail.com
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Critères de participation
Les chercheurs recherchent des personnes qui correspondent à une certaine description, appelée critères d'éligibilité. Certains exemples de ces critères sont l'état de santé général d'une personne ou des traitements antérieurs.
Critère d'éligibilité
Âges éligibles pour étudier
- Adulte
- Adulte plus âgé
Accepte les volontaires sains
Non
La description
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
Plan d'étude
Cette section fournit des détails sur le plan d'étude, y compris la façon dont l'étude est conçue et ce que l'étude mesure.
Comment l'étude est-elle conçue ?
Détails de conception
- Objectif principal: Diagnostique
- Répartition: Randomisé
- Modèle interventionnel: Affectation parallèle
- Masquage: Aucun (étiquette ouverte)
Armes et Interventions
Groupe de participants / Bras |
Intervention / Traitement |
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Expérimental: 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.
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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.
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Comparateur actif: 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.
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Routine emergency department diagnostic evaluation performed according to standard clinical practice without AI-assisted diagnostic support.
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Que mesure l'étude ?
Principaux critères de jugement
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
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Diagnostic concordance between the final emergency department diagnosis and the blinded adjudicated reference diagnosis established at hospital discharge.
Délai: During the index hospitalization, up to hospital discharge (average 3 days)
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Diagnostic concordance between the treating physician's final emergency department diagnosis and the blinded adjudicated reference diagnosis based on the prespecified principal diagnostic category.
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During the index hospitalization, up to hospital discharge (average 3 days)
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Mesures de résultats secondaires
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
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Diagnostic concordance after Generative Pre-trained Transformer (GPT)-assisted diagnostic support
Délai: During the index emergency department visit (average 6 hours)
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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.
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During the index emergency department visit (average 6 hours)
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Time from emergency department presentation to final diagnosis
Délai: During the index emergency department visit (average 6 hours)
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Time required from emergency department presentation until establishment of the physician's final emergency department diagnosis.
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During the index emergency department visit (average 6 hours)
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Diagnostic reclassification after Generative Pre-trained Transformer (GPT)-assisted evaluation
Délai: During the index emergency department visit (average 6 hours)
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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.
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During the index emergency department visit (average 6 hours)
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Physician diagnostic confidence
Délai: During the index emergency department visit (average 6 hours)
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Physician-reported diagnostic confidence recorded before and after Generative Pre-trained Transformer (GPT)-assisted diagnostic support using the prespecified study assessment scale.
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During the index emergency department visit (average 6 hours)
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Physician acceptance of Generative Pre-trained Transformer (GPT)-generated diagnostic recommendations
Délai: During the index emergency department visit (average 6 hours)
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Frequency of physician acceptance, modification, or rejection of Generative Pre-trained Transformer (GPT)-generated diagnostic recommendations in the intervention group.
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During the index emergency department visit (average 6 hours)
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Emergency department disposition accuracy
Délai: Up to hospital discharge (average 3 days)
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Accuracy of emergency department disposition decisions, including discharge, hospital admission, or intensive care unit admission, compared with the adjudicated reference diagnosis.
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Up to hospital discharge (average 3 days)
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Emergency department length of stay
Délai: Up to hospital discharge (average 3 days)
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Length of stay in the emergency department measured from patient presentation until emergency department discharge or hospital admission.
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Up to hospital discharge (average 3 days)
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Hospital length of stay
Délai: Up to hospital discharge (average 3 days)
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Total duration of hospitalization from admission until hospital discharge.
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Up to hospital discharge (average 3 days)
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In-hospital mortality
Délai: Up to hospital discharge (average 3 days)
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All-cause mortality occurring during the index hospitalization.
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Up to hospital discharge (average 3 days)
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30-day all-cause mortality
Délai: 30 days
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All-cause mortality occurring within 30 days after the index emergency department visit.
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30 days
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30-day emergency department revisit
Délai: 30 days
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Revisit to any emergency department for any cause within 30 days after the index emergency department visit.
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30 days
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30-day hospital readmission
Délai: 30 days
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Hospital readmission for any cause within 30 days after discharge from the index hospitalization.
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30 days
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Collaborateurs et enquêteurs
C'est ici que vous trouverez les personnes et les organisations impliquées dans cette étude.
Parrainer
Collaborateurs
Publications et liens utiles
La personne responsable de la saisie des informations sur l'étude fournit volontairement ces publications. Il peut s'agir de tout ce qui concerne l'étude.
Publications générales
- Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019 Jan;25(1):44-56. doi: 10.1038/s41591-018-0300-7. Epub 2019 Jan 7.
- Lopez-Puerta JM, Fernandez-Marin MR, Martin Benlloch JA, Lorente R. Spinal osteoid osteoma recurring as an aggressive osteoblastoma. Neurocirugia (Engl Ed). 2020 May-Jun;31(3):146-150. doi: 10.1016/j.neucir.2019.06.002. Epub 2019 Sep 2. English, Spanish.
- ANCA-associated vasculitis. Nat Rev Dis Primers. 2020 Aug 27;6(1):72. doi: 10.1038/s41572-020-0212-y. No abstract available.
- Kim TH, Kim CH, Choi SG. Radiation-induced angiosarcoma (RIAS) of the maxilla: a case report. J Korean Assoc Oral Maxillofac Surg. 2020 Aug 31;46(4):288-291. doi: 10.5125/jkaoms.2020.46.4.288.
- Li R, Chen X, Wang Y. Adverse events analysis of Relugolix (Orgovyx(R)) for prostate cancer based on the FDA Adverse Event Reporting System (FAERS). PLoS One. 2024 Oct 22;19(10):e0312481. doi: 10.1371/journal.pone.0312481. eCollection 2024.
- Asravor RK. Uncovering the forgotten story of the impact of Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome on economic growth in Ghana: A gender analysis. Int J Health Plann Manage. 2023 Sep;38(5):1495-1509. doi: 10.1002/hpm.3675. Epub 2023 Jun 23.
- Shakiba M, Nazemipour M, Mansournia N, Mansournia MA. Protective effect of intensive glucose lowering therapy on all-cause mortality, adjusted for treatment switching using G-estimation method, the ACCORD trial. Sci Rep. 2023 Apr 10;13(1):5833. doi: 10.1038/s41598-023-32855-3.
- Hsu HW, Chiu MC, Shoemaker D, Yang CS. Viral infections in fire ants lead to reduced foraging activity and dietary changes. Sci Rep. 2018 Sep 10;8(1):13498. doi: 10.1038/s41598-018-31969-3.
Dates d'enregistrement des études
Ces dates suivent la progression des dossiers d'étude et des soumissions de résultats sommaires à ClinicalTrials.gov. Les dossiers d'étude et les résultats rapportés sont examinés par la Bibliothèque nationale de médecine (NLM) pour s'assurer qu'ils répondent à des normes de contrôle de qualité spécifiques avant d'être publiés sur le site Web public.
Dates principales de l'étude
Début de l'étude (Estimé)
1 janvier 2027
Achèvement primaire (Estimé)
31 décembre 2028
Achèvement de l'étude (Estimé)
31 décembre 2029
Dates d'inscription aux études
Première soumission
18 juillet 2026
Première soumission répondant aux critères de contrôle qualité
21 juillet 2026
Première publication (Réel)
27 juillet 2026
Mises à jour des dossiers d'étude
Dernière mise à jour publiée (Réel)
27 juillet 2026
Dernière mise à jour soumise répondant aux critères de contrôle qualité
21 juillet 2026
Dernière vérification
1 juillet 2026
Plus d'information
Termes liés à cette étude
Mots clés
Termes MeSH pertinents supplémentaires
Autres numéros d'identification d'étude
- ER-VISION-AI study
Plan pour les données individuelles des participants (IPD)
Prévoyez-vous de partager les données individuelles des participants (DPI) ?
INDÉCIS
Description du régime IPD
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.
Informations sur les médicaments et les dispositifs, documents d'étude
Étudie un produit pharmaceutique réglementé par la FDA américaine
Non
Étudie un produit d'appareil réglementé par la FDA américaine
Non
Ces informations ont été extraites directement du site Web clinicaltrials.gov sans aucune modification. Si vous avez des demandes de modification, de suppression ou de mise à jour des détails de votre étude, veuillez contacter register@clinicaltrials.gov. Dès qu'un changement est mis en œuvre sur clinicaltrials.gov, il sera également mis à jour automatiquement sur notre site Web .