ER-VISION-AI Study

NCT07727590 | Not Yet Recruiting DMC

• 1 other identifier: ER-VISION-AI study
• Study Type: interventional
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Chest Pain; Dyspnea; Emergency Department Patients; Acute Cardiopulmonary Disease

Keywords

Artificial intelligence; Large language model; Emergency department; Electrocardiography; Chest radiography; Multimodal AI; Clinical decision support; Randomized controlled trial

Outcome Measures

Primary Outcomes (1)

• Diagnostic concordance between the final emergency department diagnosis and the blinded adjudicated reference diagnosis established at hospital discharge.

  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)

Secondary Outcomes (12)

• Diagnostic concordance after Generative Pre-trained Transformer (GPT)-assisted diagnostic support

  During the index emergency department visit (average 6 hours)

• Time from emergency department presentation to final diagnosis

  During the index emergency department visit (average 6 hours)

• Diagnostic reclassification after Generative Pre-trained Transformer (GPT)-assisted evaluation

  During the index emergency department visit (average 6 hours)

• Physician diagnostic confidence

  During the index emergency department visit (average 6 hours)

• Physician acceptance of Generative Pre-trained Transformer (GPT)-generated diagnostic recommendations

  During the index emergency department visit (average 6 hours)

Study Arms (2)

GPT-assisted multimodal visual language model (VLM) diagnostic strategy

EXPERIMENTAL

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.

Diagnostic Test: Generative Pre-trained Transformer (GPT)-assisted multimodal visual language model (VLM) diagnostic support

Conventional physician-guided diagnostic strategy

ACTIVE COMPARATOR

Participants undergo standard emergency department diagnostic evaluation according to routine clinical practice without Generative Pre-trained Transformer (GPT)-assisted diagnostic support.

Diagnostic Test: Conventional emergency department diagnostic evaluation

Interventions

Generative Pre-trained Transformer (GPT)-assisted multimodal visual language model (VLM) diagnostic support DIAGNOSTIC_TEST

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.

GPT-assisted multimodal visual language model (VLM) diagnostic strategy

Conventional emergency department diagnostic evaluation DIAGNOSTIC_TEST

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

Conventional physician-guided diagnostic strategy

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Ewha Womans University Mokdong Hospital: lead
• Ewha Womans University Seoul Hospital: collaborator

Study Sites (1)

Ewha Womans University Mokdong Hospital

Seoul, 07804, South Korea

Location

Related Publications (8)

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

MeSH Terms

Conditions

Chest Pain; Dyspnea; Emergencies

Condition Hierarchy (Ancestors)

Pain; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Respiration Disorders; Respiratory Tract Diseases; Signs and Symptoms, Respiratory; Disease Attributes; Pathologic Processes

Central Study Contacts

Yeji Kim, PhD

CONTACT

+82-10-2724-7740; lexie6169@gmail.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Masking Details: Outcome assessor blinded
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: Eligible participants presenting to the emergency department with acute cardiopulmonary symptoms are randomly assigned in a 1:1 ratio to either conventional physician-guided diagnostic evaluation or physician-supervised GPT-assisted multimodal diagnostic support. Randomization is performed immediately after completion of the initial clinical assessment and documentation of the physician's preliminary diagnosis.

Study Record Dates

• First Submitted: July 18, 2026
• First Posted: July 27, 2026
• Study Start (Estimated): January 1, 2027
• Primary Completion (Estimated): December 31, 2028
• Study Completion (Estimated): December 31, 2029
• Last Updated: July 27, 2026

Record last verified: 2026-07

Locations

KR (1)