ER-VISION-AI Study (ER-VISION-AI)
2026年7月21日 更新者：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.
調査の概要
状態
まだ募集していません
研究の種類
介入
入学 (推定)
1000
段階
- 適用できない
連絡先と場所
このセクションには、調査を実施する担当者の連絡先の詳細と、この調査が実施されている場所に関する情報が記載されています。
研究連絡先
- 名前：Yeji Kim, PhD
- 電話番号：+82-10-2724-7740
- メール：lexie6169@gmail.com
研究場所
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-
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Seoul、韓国、07804
- Ewha Womans University Mokdong Hospital
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コンタクト：
- Yeji Kim, PhD
- 電話番号：+82-10-2724-7740
- メール：lexie6169@gmail.com
-
-
参加基準
研究者は、適格基準と呼ばれる特定の説明に適合する人を探します。これらの基準のいくつかの例は、人の一般的な健康状態または以前の治療です。
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
いいえ
説明
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
研究計画
このセクションでは、研究がどのように設計され、研究が何を測定しているかなど、研究計画の詳細を提供します。
研究はどのように設計されていますか？
デザインの詳細
- 主な目的：診断
- 割り当て：ランダム化
- 介入モデル：並列代入
- マスキング：なし（オープンラベル）
武器と介入
参加者グループ / アーム |
介入・治療 |
|---|---|
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実験的：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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アクティブコンパレータ：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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この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Diagnostic concordance between the final emergency department diagnosis and the blinded adjudicated reference diagnosis established at hospital discharge.
時間枠：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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二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Diagnostic concordance after Generative Pre-trained Transformer (GPT)-assisted diagnostic support
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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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協力者と研究者
ここでは、この調査に関係する人々や組織を見つけることができます。
出版物と役立つリンク
研究に関する情報を入力する責任者は、自発的にこれらの出版物を提供します。これらは、研究に関連するあらゆるものに関するものである可能性があります。
一般刊行物
- 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.
研究記録日
これらの日付は、ClinicalTrials.gov への研究記録と要約結果の提出の進捗状況を追跡します。研究記録と報告された結果は、国立医学図書館 (NLM) によって審査され、公開 Web サイトに掲載される前に、特定の品質管理基準を満たしていることが確認されます。
主要日程の研究
研究開始 (推定)
2027年1月1日
一次修了 (推定)
2028年12月31日
研究の完了 (推定)
2029年12月31日
試験登録日
最初に提出
2026年7月18日
QC基準を満たした最初の提出物
2026年7月21日
最初の投稿 (実際)
2026年7月27日
学習記録の更新
投稿された最後の更新 (実際)
2026年7月27日
QC基準を満たした最後の更新が送信されました
2026年7月21日
最終確認日
2026年7月1日
詳しくは
本研究に関する用語
その他の研究ID番号
- ER-VISION-AI study
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
未定
IPD プランの説明
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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いいえ
米国FDA規制機器製品の研究
いいえ
この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。