[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100648876":3},{"organization":4,"armGroups":7,"interventions":20,"overallOfficials":26,"centralContacts":31,"locations":37,"responsibleParty":50,"collaborators":52,"id":55,"slug":56,"hasResults":57,"nctId":58,"briefTitle":59,"officialTitle":60,"acronym":61,"eligibilityCriteria":62,"healthyVolunteers":57,"sex":63,"minAge":64,"maxAge":26,"enrollmentInfo":65,"targetDuration":26,"studyType":68,"phases":69,"briefSummary":71,"conditions":72,"keywords":77,"overallStatus":86,"whyStopped":26,"lastUpdateSubmitDate":87,"lastUpdatePostDateStruct":88,"startDateStruct":91,"completionDateStruct":93,"leadSponsor":95,"locationsCount":96},{"fullName":5,"class":6},"Ewha Womans University Mokdong Hospital","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"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.",[13],"Diagnostic Test: Generative Pre-trained Transformer (GPT)-assisted multimodal visual language model (VLM) diagnostic support",{"label":15,"type":16,"description":17,"interventionNames":18},"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.",[19],"Diagnostic Test: Conventional emergency department diagnostic evaluation",[21,27],{"type":22,"name":23,"description":24,"armGroupLabels":25,"otherNames":26},"DIAGNOSTIC_TEST","Generative Pre-trained Transformer (GPT)-assisted multimodal visual language model (VLM) diagnostic support","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.",[9],null,{"type":22,"name":28,"description":29,"armGroupLabels":30,"otherNames":26},"Conventional emergency department diagnostic evaluation","Routine emergency department diagnostic evaluation performed according to standard clinical practice without AI-assisted diagnostic support.",[15],[32],{"name":33,"role":34,"phone":35,"phoneExt":26,"email":36},"Yeji Kim, PhD","CONTACT","+82-10-2724-7740","lexie6169@gmail.com",[38],{"facility":5,"status":26,"city":39,"state":26,"zip":40,"country":41,"countryCode":26,"cosmosGeoPoint":42,"geoPoint":47,"contacts":48},"Seoul","07804","South Korea",{"type":43,"coordinates":44},"Point",[45,46],126.9784,37.566,{"lat":46,"lon":45},[49],{"name":33,"role":34,"phone":35,"phoneExt":26,"email":36},{"type":51,"investigatorFullName":26,"investigatorTitle":26,"investigatorAffiliation":26,"oldNameTitle":26,"oldOrganization":26},"SPONSOR",[53],{"name":54,"class":6},"Ewha Womans University Seoul Hospital","100648876","er-vision-ai-study-100648876",false,"NCT07727590","ER-VISION-AI Study","Multimodal Visual Language Model-Assisted Diagnostic Strategy in the Emergency Department: A Prospective Multicenter Randomized Controlled Trial (ER-VISION-AI Study)","ER-VISION-AI","Inclusion Criteria:\n\n* Age ≥18 years\n* Presentation to a participating emergency department with acute cardiopulmonary symptoms, including chest pain, dyspnea, palpitations, syncope, dizziness, or fever accompanied by cardiopulmonary symptoms\n* Performance of both a standard 12-lead electrocardiogram and chest radiography during the initial emergency department evaluation\n* Availability of initial clinical assessment, vital signs, laboratory findings, and all mandatory clinical information required for the multimodal AI workflow\n* Expected emergency department observation or hospital admission for at least 24 hours\n* Ability and willingness to provide written informed consent\n\nExclusion Criteria:\n\n* Inability or refusal to provide written informed consent\n* Requirement for immediate life-saving intervention that precludes completion of the study workflow\n* Death before completion of the initial emergency department diagnostic assessment\n* Electrocardiographic quality insufficient for reliable physician or Artificial intelligence (AI) interpretation\n* Chest radiographic quality insufficient for reliable physician or Artificial intelligence (AI) interpretation\n* Cardiac pacing rhythm\n* Missing mandatory clinical information required for the multimodal Artificial intelligence (AI) workflow\n* Previous enrollment in the ER-VISION-AI trial\n* Inability to establish a blinded adjudicated reference diagnosis","ALL","18 Years",{"count":66,"type":67},1000,"ESTIMATED","INTERVENTIONAL",[70],"NA","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.",[73,74,75,76],"Chest Pain","Dyspnea","Acute Cardiopulmonary Disease","Emergency Department Patients",[78,79,80,81,82,83,84,85],"Artificial intelligence","Large language model","Emergency department","Electrocardiography","Chest radiography","Multimodal AI","Clinical decision support","Randomized controlled trial","NOT_YET_RECRUITING","2026-07-21",{"date":89,"type":90},"2026-07-27","ACTUAL",{"date":92,"type":67},"2027-01-01",{"date":94,"type":67},"2029-12-31",{"name":5,"class":6},1]