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ER-VISION-AI Study

No phase Interventional Chest Pain Dyspnea Acute Cardiopulmonary Disease Emergency Department Patients

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Generative Pre-trained Transformer (GPT)-assisted multimodal visual language model (VLM) diagnostic support, Conventional emergency department diagnostic evaluation.
Who it may be relevant to
Registry conditions: Chest Pain, Dyspnea, Acute Cardiopulmonary Disease, Emergency Department Patients. Basic parameters: from 18 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
South Korea
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

Official title

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

Overview

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.

Interventions

  • Diagnostic 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.
  • Diagnostic test Conventional emergency department diagnostic evaluation
    Routine emergency department diagnostic evaluation performed according to standard clinical practice without AI-assisted diagnostic support.

Primary outcome measures

  • Diagnostic concordance between the final emergency department diagnosis and the blinded adjudicated reference diagnosis established at hospital discharge. [Time frame: During the index hospitalization, up to hospital discharge (average 3 days)]
Secondary outcome measures (12)
  • Diagnostic concordance after Generative Pre-trained Transformer (GPT)-assisted diagnostic support [Time frame: During the index emergency department visit (average 6 hours)]
  • Time from emergency department presentation to final diagnosis [Time frame: During the index emergency department visit (average 6 hours)]
  • Diagnostic reclassification after Generative Pre-trained Transformer (GPT)-assisted evaluation [Time frame: During the index emergency department visit (average 6 hours)]
  • Physician diagnostic confidence [Time frame: During the index emergency department visit (average 6 hours)]
  • Physician acceptance of Generative Pre-trained Transformer (GPT)-generated diagnostic recommendations [Time frame: During the index emergency department visit (average 6 hours)]
  • Emergency department disposition accuracy [Time frame: Up to hospital discharge (average 3 days)]
  • Emergency department length of stay [Time frame: Up to hospital discharge (average 3 days)]
  • Hospital length of stay [Time frame: Up to hospital discharge (average 3 days)]
  • In-hospital mortality [Time frame: Up to hospital discharge (average 3 days)]
  • 30-day all-cause mortality [Time frame: 30 days]
  • 30-day emergency department revisit [Time frame: 30 days]
  • 30-day hospital readmission [Time frame: 30 days]

Eligibility criteria

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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation
Randomized
Model
Parallel assignment
Masking
Open label
Primary purpose
Diagnostic

Study locations

South Korea · 1 center
  • Ewha Womans University Mokdong Hospital — Seoul

Publications

  • 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. PMID 31488355
  • ANCA-associated vasculitis. Nat Rev Dis Primers. 2020 Aug 27;6(1):72. doi: 10.1038/s41572-020-0212-y. No abstract available. PMID 32855427
  • 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. PMID 32855377
  • 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. PMID 39436909
  • 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. PMID 37353922
  • 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. PMID 37037931
  • 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. PMID 30617339
  • 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. PMID 30202033

Identifiers

NCT: NCT07727590 · ER-VISION-AI study

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗