ER-VISION-AI Study (ER-VISION-AI)
21 de julio de 2026 actualizado por: 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.
Descripción general del estudio
Estado
Aún no reclutando
Condiciones
Tipo de estudio
Intervencionista
Inscripción (Estimado)
1000
Fase
- No aplica
Contactos y Ubicaciones
Esta sección proporciona los datos de contacto de quienes realizan el estudio e información sobre dónde se lleva a cabo este estudio.
Estudio Contacto
- Nombre: Yeji Kim, PhD
- Número de teléfono: +82-10-2724-7740
- Correo electrónico: lexie6169@gmail.com
Ubicaciones de estudio
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Seoul, Corea del Sur, 07804
- Ewha Womans University Mokdong Hospital
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Contacto:
- Yeji Kim, PhD
- Número de teléfono: +82-10-2724-7740
- Correo electrónico: lexie6169@gmail.com
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Criterios de participación
Los investigadores buscan personas que se ajusten a una determinada descripción, denominada criterio de elegibilidad. Algunos ejemplos de estos criterios son el estado de salud general de una persona o tratamientos previos.
Criterio de elegibilidad
Edades elegibles para estudiar
- Adulto
- Adulto Mayor
Acepta Voluntarios Saludables
No
Descripción
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 de estudios
Esta sección proporciona detalles del plan de estudio, incluido cómo está diseñado el estudio y qué mide el estudio.
¿Cómo está diseñado el estudio?
Detalles de diseño
- Propósito principal: Diagnóstico
- Asignación: Aleatorizado
- Modelo Intervencionista: Asignación paralela
- Enmascaramiento: Ninguno (etiqueta abierta)
Armas e Intervenciones
Grupo de participantes/brazo |
Intervención / Tratamiento |
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Experimental: 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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Comparador activo: 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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¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
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Diagnostic concordance between the final emergency department diagnosis and the blinded adjudicated reference diagnosis established at hospital discharge.
Periodo de tiempo: 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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Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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Diagnostic concordance after Generative Pre-trained Transformer (GPT)-assisted diagnostic support
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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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Colaboradores e Investigadores
Aquí es donde encontrará personas y organizaciones involucradas en este estudio.
Patrocinador
Colaboradores
Publicaciones y enlaces útiles
La persona responsable de ingresar información sobre el estudio proporciona voluntariamente estas publicaciones. Estos pueden ser sobre cualquier cosa relacionada con el estudio.
Publicaciones Generales
- 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.
Fechas de registro del estudio
Estas fechas rastrean el progreso del registro del estudio y los envíos de resultados resumidos a ClinicalTrials.gov. Los registros del estudio y los resultados informados son revisados por la Biblioteca Nacional de Medicina (NLM) para asegurarse de que cumplan con los estándares de control de calidad específicos antes de publicarlos en el sitio web público.
Fechas importantes del estudio
Inicio del estudio (Estimado)
1 de enero de 2027
Finalización primaria (Estimado)
31 de diciembre de 2028
Finalización del estudio (Estimado)
31 de diciembre de 2029
Fechas de registro del estudio
Enviado por primera vez
18 de julio de 2026
Primero enviado que cumplió con los criterios de control de calidad
21 de julio de 2026
Publicado por primera vez (Actual)
27 de julio de 2026
Actualizaciones de registros de estudio
Última actualización publicada (Actual)
27 de julio de 2026
Última actualización enviada que cumplió con los criterios de control de calidad
21 de julio de 2026
Última verificación
1 de julio de 2026
Más información
Términos relacionados con este estudio
Palabras clave
Términos MeSH relevantes adicionales
Otros números de identificación del estudio
- ER-VISION-AI study
Plan de datos de participantes individuales (IPD)
¿Planea compartir datos de participantes individuales (IPD)?
INDECISO
Descripción del plan 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.
Información sobre medicamentos y dispositivos, documentos del estudio
Estudia un producto farmacéutico regulado por la FDA de EE. UU.
No
Estudia un producto de dispositivo regulado por la FDA de EE. UU.
No
Esta información se obtuvo directamente del sitio web clinicaltrials.gov sin cambios. Si tiene alguna solicitud para cambiar, eliminar o actualizar los detalles de su estudio, comuníquese con register@clinicaltrials.gov. Tan pronto como se implemente un cambio en clinicaltrials.gov, también se actualizará automáticamente en nuestro sitio web. .