ER-VISION-AI Study (ER-VISION-AI)
2026년 7월 21일 업데이트: Ewha Womans University Mokdong Hospital
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.
연구 개요
상태
아직 모집하지 않음
연구 유형
중재적
등록 (추정된)
1000
단계
- 해당 없음
연락처 및 위치
이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.
연구 연락처
- 이름: Yeji Kim, PhD
- 전화번호: +82-10-2724-7740
- 이메일: lexie6169@gmail.com
연구 장소
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Seoul, 대한민국, 07804
- Ewha Womans University Mokdong Hospital
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연락하다:
- Yeji Kim, PhD
- 전화번호: +82-10-2724-7740
- 이메일: lexie6169@gmail.com
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참여기준
연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.
자격 기준
공부할 수 있는 나이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
아니
설명
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
공부 계획
이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.
연구는 어떻게 설계됩니까?
디자인 세부사항
- 주 목적: 특수 증상
- 할당: 무작위
- 중재 모델: 병렬 할당
- 마스킹: 없음(오픈 라벨)
무기와 개입
참가자 그룹 / 팔 |
개입 / 치료 |
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실험적: GPT-assisted multimodal visual language model (VLM) diagnostic strategy
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.
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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.
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활성 비교기: Conventional physician-guided diagnostic strategy
Participants undergo standard emergency department diagnostic evaluation according to routine clinical practice without Generative Pre-trained Transformer (GPT)-assisted diagnostic support.
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Routine emergency department diagnostic evaluation performed according to standard clinical practice without AI-assisted diagnostic support.
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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Diagnostic concordance between the final emergency department diagnosis and the blinded adjudicated reference diagnosis established at hospital discharge.
기간: During the index hospitalization, up to hospital discharge (average 3 days)
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Diagnostic concordance between the treating physician's final emergency department diagnosis and the blinded adjudicated reference diagnosis based on the prespecified principal diagnostic category.
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During the index hospitalization, up to hospital discharge (average 3 days)
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2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Diagnostic concordance after Generative Pre-trained Transformer (GPT)-assisted diagnostic support
기간: During the index emergency department visit (average 6 hours)
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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.
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During the index emergency department visit (average 6 hours)
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Time from emergency department presentation to final diagnosis
기간: During the index emergency department visit (average 6 hours)
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Time required from emergency department presentation until establishment of the physician's final emergency department diagnosis.
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During the index emergency department visit (average 6 hours)
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Diagnostic reclassification after Generative Pre-trained Transformer (GPT)-assisted evaluation
기간: During the index emergency department visit (average 6 hours)
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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.
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During the index emergency department visit (average 6 hours)
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Physician diagnostic confidence
기간: During the index emergency department visit (average 6 hours)
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Physician-reported diagnostic confidence recorded before and after Generative Pre-trained Transformer (GPT)-assisted diagnostic support using the prespecified study assessment scale.
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During the index emergency department visit (average 6 hours)
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Physician acceptance of Generative Pre-trained Transformer (GPT)-generated diagnostic recommendations
기간: During the index emergency department visit (average 6 hours)
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Frequency of physician acceptance, modification, or rejection of Generative Pre-trained Transformer (GPT)-generated diagnostic recommendations in the intervention group.
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During the index emergency department visit (average 6 hours)
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Emergency department disposition accuracy
기간: Up to hospital discharge (average 3 days)
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Accuracy of emergency department disposition decisions, including discharge, hospital admission, or intensive care unit admission, compared with the adjudicated reference diagnosis.
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Up to hospital discharge (average 3 days)
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Emergency department length of stay
기간: Up to hospital discharge (average 3 days)
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Length of stay in the emergency department measured from patient presentation until emergency department discharge or hospital admission.
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Up to hospital discharge (average 3 days)
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Hospital length of stay
기간: Up to hospital discharge (average 3 days)
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Total duration of hospitalization from admission until hospital discharge.
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Up to hospital discharge (average 3 days)
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In-hospital mortality
기간: Up to hospital discharge (average 3 days)
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All-cause mortality occurring during the index hospitalization.
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Up to hospital discharge (average 3 days)
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30-day all-cause mortality
기간: 30 days
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All-cause mortality occurring within 30 days after the index emergency department visit.
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30 days
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30-day emergency department revisit
기간: 30 days
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Revisit to any emergency department for any cause within 30 days after the index emergency department visit.
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30 days
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30-day hospital readmission
기간: 30 days
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Hospital readmission for any cause within 30 days after discharge from the index hospitalization.
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30 days
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공동 작업자 및 조사자
여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.
간행물 및 유용한 링크
연구에 대한 정보 입력을 담당하는 사람이 자발적으로 이러한 간행물을 제공합니다. 이것은 연구와 관련된 모든 것에 관한 것일 수 있습니다.
일반 간행물
- 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.
- 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.
- ANCA-associated vasculitis. Nat Rev Dis Primers. 2020 Aug 27;6(1):72. doi: 10.1038/s41572-020-0212-y. No abstract available.
- 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.
- 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.
- 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.
- 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.
- 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.
연구 기록 날짜
이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.
연구 주요 날짜
연구 시작 (추정된)
2027년 1월 1일
기본 완료 (추정된)
2028년 12월 31일
연구 완료 (추정된)
2029년 12월 31일
연구 등록 날짜
최초 제출
2026년 7월 18일
QC 기준을 충족하는 최초 제출
2026년 7월 21일
처음 게시됨 (실제)
2026년 7월 27일
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
2026년 7월 27일
QC 기준을 충족하는 마지막 업데이트 제출
2026년 7월 21일
마지막으로 확인됨
2026년 7월 1일
추가 정보
이 연구와 관련된 용어
추가 관련 MeSH 약관
기타 연구 ID 번호
- ER-VISION-AI study
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
미정
IPD 계획 설명
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.
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
아니
미국 FDA 규제 기기 제품 연구
아니
이 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .