Chest PainER-VISION-AINCT07727590
ER-VISION-AI Study
A study testing Generative Pre-trained Transformer (GPT)-assisted multimodal visual language model (VLM) diagnostic support and Conventional emergency department diagnostic evaluation in people with Chest Pain and Dyspnea, run by Ewha Womans University Mokdong Hospital at 1 site in South Korea.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Chest Pain, Dyspnea, Acute Cardiopulmonary Disease
- What is being tested?
- Generative Pre-trained Transformer (GPT)-assisted multimodal visual language model (VLM) diagnostic support and Conventional emergency department diagnostic evaluation
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,000 participants
- Where
- 1 siteSouth Korea
- Study dates
- 1 Jan 2027to 31 Dec 2029
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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
Show all 9Show fewer
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
South Korea1 site
Ewha Womans University Mokdong Hospital
Seoul, 07804
Yeji Kim, PhD, +82-10-2724-7740, lexie6169@gmail.com
About this study
Official title: 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.
- Being tested
- Generative Pre-trained Transformer (GPT)-assisted multimodal visual language model (VLM) diagnostic support, Conventional emergency department diagnostic evaluation
- Keywords
- Artificial intelligence, Large language model, Emergency department, Electrocardiography, Chest radiography, Multimodal AI, Clinical decision support, Randomized controlled trial
- Medical subject headings
- Chest Pain, Dyspnea, Emergencies
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Generative Pre-trained Transformer (GPT)-assisted multimodal visual language model (VLM) diagnostic supportDiagnostic 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 evaluationDiagnostic test
Routine emergency department diagnostic evaluation performed according to standard clinical practice without AI-assisted diagnostic support.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: Diagnostic Test: Conventional emergency department diagnostic evaluation |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
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.
Measured: During the index emergency department visit (average 6 hours)
Time from emergency department presentation to final diagnosis
Time required from emergency department presentation until establishment of the physician's final emergency department diagnosis.
Measured: During the index emergency department visit (average 6 hours)
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.
Measured: During the index emergency department visit (average 6 hours)
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.
Measured: During the index emergency department visit (average 6 hours)
Physician acceptance of Generative Pre-trained Transformer (GPT)-generated diagnostic recommendations
Frequency of physician acceptance, modification, or rejection of Generative Pre-trained Transformer (GPT)-generated diagnostic recommendations in the intervention group.
Measured: During the index emergency department visit (average 6 hours)
Emergency department disposition accuracy
Accuracy of emergency department disposition decisions, including discharge, hospital admission, or intensive care unit admission, compared with the adjudicated reference diagnosis.
Measured: Up to hospital discharge (average 3 days)
Emergency department length of stay
Length of stay in the emergency department measured from patient presentation until emergency department discharge or hospital admission.
Measured: Up to hospital discharge (average 3 days)
Hospital length of stay
Total duration of hospitalization from admission until hospital discharge.
Measured: Up to hospital discharge (average 3 days)
In-hospital mortality
All-cause mortality occurring during the index hospitalization.
Measured: Up to hospital discharge (average 3 days)
30-day all-cause mortality
All-cause mortality occurring within 30 days after the index emergency department visit.
Measured: 30 days
30-day emergency department revisit
Revisit to any emergency department for any cause within 30 days after the index emergency department visit.
Measured: 30 days
30-day hospital readmission
Hospital readmission for any cause within 30 days after discharge from the index hospitalization.
Measured: 30 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Jul 2026
- Study start1 Jan 2027 (estimated)
- Primary completion31 Dec 2028 (estimated)
- Study completion31 Dec 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for Chest PainStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- 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.
- Masking
- None (open label)
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Ewha Womans University Mokdong Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Ewha Womans University Seoul Hospital
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07727590
- Sponsor's protocol ID
- ER-VISION-AI study
- Acronym
- ER-VISION-AI
- Status last verified
- Jul 2026
- First submitted
- 18 Jul 2026
- Last updated
- 27 Jul 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: 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.
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.
PubMed 31488355ANCA-associated vasculitis. Nat Rev Dis Primers. 2020 Aug 27;6(1):72. doi: 10.1038/s41572-020-0212-y. No abstract available.
PubMed 32855427Kim 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.
PubMed 32855377Li 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.
PubMed 39436909Asravor 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.
PubMed 37353922Shakiba 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.
PubMed 37037931Topol 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.
PubMed 30617339Hsu 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.
PubMed 30202033
Study at a glance
From the registry record, last updated by the sponsor on 27 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multimodal Visual Language Model-Assisted Diagnostic Strategy in the Emergency Department: A Prospective Multicenter Randomized Controlled Trial (ER-VISION-AI Study)
- ClinicalTrials.gov ID
- NCT07727590
- Acronym / sponsor ID
- ER-VISION-AI / ER-VISION-AI study
- Keywords
- Artificial intelligence, Large language model, Emergency department, Electrocardiography, Chest radiography, Multimodal AI, Clinical decision support, Randomized controlled trial
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Ewha Womans University Mokdong HospitalUniversity, hospital or other
- Collaborators
- Ewha Womans University Seoul Hospital
- Study start
- 1 Jan 2027 (estimated)
- Primary completion
- 31 Dec 2028 (estimated)
- Study completion
- 31 Dec 2029 (estimated)
- First posted
- 27 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- 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.
| Arm | Type | Receives |
|---|---|---|
| GPT-assisted multimodal visual language model (VLM) diagnostic strategy | Experimental | Diagnostic Test: Generative Pre-trained Transformer (GPT)-assisted multimodal visual language model (VLM) diagnostic supportParticipants 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. |
| Conventional physician-guided diagnostic strategy | Active comparator | Diagnostic Test: Conventional emergency department diagnostic evaluationParticipants undergo standard emergency department diagnostic evaluation according to routine clinical practice without Generative Pre-trained Transformer (GPT)-assisted diagnostic support. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- 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
Key exclusion criteria (9 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Generative Pre-trained Transformer (GPT)-assisted multimodal visual language model (VLM) diagnostic supportA 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 | GPT-assisted multimodal visual language model (VLM) diagnostic strategy |
| Conventional emergency department diagnostic evaluationRoutine emergency department diagnostic evaluation performed according to standard clinical practice without AI-assisted diagnostic support. | Diagnostic test | Conventional physician-guided diagnostic strategy |
Primary outcome
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.
12 secondary outcomes
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Jul 2026.
- Background: PubMed 31488355Lopez-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.
- Background: PubMed 32855427ANCA-associated vasculitis. Nat Rev Dis Primers. 2020 Aug 27;6(1):72. doi: 10.1038/s41572-020-0212-y. No abstract available.
- Background: PubMed 32855377Kim 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.
- Background: PubMed 39436909Li 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.
- Background: PubMed 37353922Asravor 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.
- Background: PubMed 37037931Shakiba 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.
- Background: PubMed 30617339Topol 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.
- Background: PubMed 30202033Hsu 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.
- Individual participant dataSharing planned: Undecided. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Generative Pre-trained Transformer (GPT)-assisted multimodal visual language model (VLM) diagnostic support and Conventional emergency department diagnostic evaluation in people with Chest Pain and Dyspnea, run by Ewha Womans University Mokdong Hospital at 1 site in South Korea.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 9 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 27 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in South Korea. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Yeji Kim, PhD (+82-10-2724-7740, lexie6169@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07727590, which also lists 8 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Jul 2026.