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ER-VISION-AI Study

ConditionsChest PainDyspneaAcute Cardiopulmonary DiseaseEmergency Department Patients
Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18+
SponsorEwha Womans University Mokdong Hospital

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

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

Disqualifiers

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

Trial design

Design model

Parallel

Treatments tested in this trial

  • 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.

Treatment groups

1,000 Participants
are divided into 2 treatment groups
Group A: GPT-assisted multimodal visual language model (VLM) diagnostic strategyExperimental treatment 1 intervention
Group B: Conventional physician-guided diagnostic strategyActive comparator 1 intervention

Trial outcomes

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.

Time frame
During the index hospitalization, up to hospital discharge (average 3 days)

Secondary outcomes

1

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

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.

Time frame
During the index emergency department visit (average 6 hours)
2

Time from emergency department presentation to final diagnosis

Time required from emergency department presentation until establishment of the physician's final emergency department diagnosis.

Time frame
During the index emergency department visit (average 6 hours)
3

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

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.

Time frame
During the index emergency department visit (average 6 hours)
4

Physician diagnostic confidence

Physician-reported diagnostic confidence recorded before and after Generative Pre-trained Transformer (GPT)-assisted diagnostic support using the prespecified study assessment scale.

Time frame
During the index emergency department visit (average 6 hours)

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Ewha Womans University Mokdong Hospital

Lead sponsor

Ewha Womans University Seoul Hospital

Collaborator

Source ClinicalTrials.gov

This trial is not recruiting at the moment. You can still explore other options: