[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"École Supérieure de Soins Ambulanciers - College of Higher Education in Prehospital Care\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":61},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":20,"targetDuration":4,"studyType":23,"phases":24,"briefSummary":26,"conditions":27,"keywords":33,"overallStatus":49,"whyStopped":4,"lastUpdateSubmitDate":50,"lastUpdatePostDateStruct":51,"startDateStruct":54,"completionDateStruct":56,"leadSponsor":58,"locationsCount":5},"100655292","smartphone-ai-assistance-for-prehospital-ecg-interpretation-100655292",false,"NCT07810686","Smartphone AI Assistance for Prehospital ECG Interpretation","AI & Prehospital ECG Analysis: A Randomized Controlled Trial of a Smartphone Large Language Model for Occlusion Myocardial Infarction Detection by Prehospital Providers","ECG-IA","Inclusion Criteria:\n\n* Prehospital care provider practising in French-speaking Switzerland\n* Any level of training: emergency medical technician, paramedic (ES), nurse (ES\u002FHES) in prehospital emergency care, or prehospital emergency physician\n* Electronic informed consent signed before randomization\n\nExclusion Criteria:\n\n* Cardiologist\n* Any person not practising in prehospital care\n* Insufficient command of written French to answer the vignettes reliably\n* Refusal to participate or withdrawal of consent",true,"ALL","18 Years",{"count":21,"type":22},144,"ESTIMATED","INTERVENTIONAL",[25],"NA","Prehospital providers interpret 12-lead electrocardiograms (ECGs) under time pressure and without immediate expert support. Missed acute coronary occlusion - occlusion myocardial infarction (OMI) - delays reperfusion, while false positive interpretations trigger unnecessary catheterization laboratory activations. Multimodal large language models (LLMs) available on any smartphone can now analyze a photographed ECG, and prehospital providers have begun using them spontaneously. No randomized trial has evaluated whether this practice improves diagnostic performance.\n\nThis randomized controlled trial compares the diagnostic performance of prehospital providers interpreting ECG clinical vignettes with and without mandatory assistance from a single, version-locked smartphone large language model. Participants - paramedics, emergency medical technicians, nurses and physicians practicing in prehospital care in French-speaking Switzerland - are randomized 1:1 on a dedicated digital platform and answer 14 clinical vignettes presented in individually randomized order. Each vignette is built around a real, anonymized 12-lead ECG obtained during routine clinical care.\n\nThe primary outcome is the proportion of vignettes for which the participant correctly identifies the presence or absence of an OMI. Secondary outcomes are sensitivity, specificity, and the accuracy of the prehospital priority decision level.",[28,29,30,31,32],"Acute Coronary Syndromes","ST Elevation Myocardial Infarction","Non-ST Elevated Myocardial Infarction","Myocardial Infarction (MI)","Diagnostic Errors",[34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"artificial intelligence","large language model","GPT-4o","electrocardiography","occlusion myocardial infarction","OMI","prehospital care","paramedics","emergency medical services","clinical decision support","diagnostic accuracy","clinical vignettes","Switzerland","French-speaking Switzerland","prehospital providers","RECRUITING","2026-09-04",{"date":52,"type":53},"2026-09-09","ACTUAL",{"date":55,"type":53},"2026-09-02",{"date":57,"type":22},"2026-11-30",{"name":59,"class":60},"École Supérieure de Soins Ambulanciers - College of Higher Education in Prehospital Care","OTHER",""]