Skip to main content
OpenTrials
Not Yet Recruiting

NCT Number: NCT07727590

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.

Not Yet Recruiting

Trial opening soon.

Get Notified

Key information

Conditions

Chest Pain Acute Cardiopulmonary Disease Disease Attributes Dyspnea Emergencies Emergency Department Patients Neurologic Manifestations Pain Pathologic Processes Pathological Conditions, Signs and Symptoms Respiration Disorders Respiratory Tract Diseases Signs and Symptoms Signs and Symptoms, Respiratory

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

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.

Conventional emergency department diagnostic evaluation

Diagnostic Test

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

Primary outcomes

  1. 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)

    Diagnostic concordance between the treating physician's final emergency department diagnosis and the blinded adjudicated reference diagnosis based on the prespecified principal diagnostic category.

Secondary outcomes

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

    Time frame: 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.

  2. Time from emergency department presentation to final diagnosis

    Time frame: 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.

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

    Time frame: 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.

  4. Physician diagnostic confidence

    Time frame: 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.

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

    Time frame: 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.

  6. Emergency department disposition accuracy

    Time frame: 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.

  7. Emergency department length of stay

    Time frame: 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.

  8. Hospital length of stay

    Time frame: Up to hospital discharge (average 3 days)

    Total duration of hospitalization from admission until hospital discharge.

  9. In-hospital mortality

    Time frame: Up to hospital discharge (average 3 days)

    All-cause mortality occurring during the index hospitalization.

  10. 30-day all-cause mortality

    Time frame: 30 days

    All-cause mortality occurring within 30 days after the index emergency department visit.

  11. 30-day emergency department revisit

    Time frame: 30 days

    Revisit to any emergency department for any cause within 30 days after the index emergency department visit.

  12. 30-day hospital readmission

    Time frame: 30 days

    Hospital readmission for any cause within 30 days after discharge from the index hospitalization.

Study contacts

Contact information is provided by the study sponsor or research team.

Yeji Kim, PhD

CONTACT

[email protected]

+82-10-2724-7740

Sponsors and collaborators

Lead sponsor

Ewha Womans University Mokdong Hospital

Other

Collaborators

  • Ewha Womans University Seoul Hospital

Registry information

Official study title

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

Acronym: ER-VISION-AI

Important dates

Study start
2027
Primary completion
2028
Study completion
2029
First posted
Jul 27, 2026
Registry last updated
Jul 27, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Related clinical trials

Published trials that share one or more normalized conditions with this study.