[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"chest-pain-rule-out-myocardial-infarction\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:chest-pain-rule-out-myocardial-infarction":28},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,2,0,[8,49],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":23,"briefSummary":25,"conditions":26,"keywords":29,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":37,"lastUpdatePostDateStruct":38,"startDateStruct":41,"completionDateStruct":43,"leadSponsor":45,"locationsCount":48},"100595087","the-rule-out-acute-myocardial-infarction-using-aritifical-intelligence-electrocardiogram-romiae-2-trial-100595087",false,"NCT07027891","The Rule-Out Acute Myocardial Infarction Using Aritifical Intelligence Electrocardiogram (ROMIAE) 2 Trial","ROMIAE 2 Trial: Randomized Controlled Trial for Managing Suspicious Acute Myocardial Infarction in ED Using AI-ECG","ROMIAE 2","Inclusion Criteria:\n\n* chest pain\n* suspicious of acute myocardial infarction\n\nExclusion Criteria:\n\n* STEMI\n* Revisit of same symptoms within 1 week\n* traumatic chest pain\n* Pneumothorax\n* Transferred from other hospital diagnosed of AMI\n* Cardiac arrest\n* Chest pain of clearly non-cardiac etiology\n* declined to participate in the study","ALL","18 Years",{"count":20,"type":21},4670,"ESTIMATED","INTERVENTIONAL",[24],"NA","This study is to see whether the AI ECG assisted protocol is as safe and efficacious as conventional protocol in early triage of suspected myocardial infarction.",[27,28],"Myocardial Infarction (MI)","Chest Pain Rule Out Myocardial Infarction",[30,31,32,33,34,35],"chest pain, suspicious myocardial infarction","artificial intelligence","electrocardiogram","emergency department","acute coronary syndrome","AI\u002FML-enabled SaMd","RECRUITING","2026-07-26",{"date":39,"type":40},"2026-07-28","ACTUAL",{"date":42,"type":40},"2025-07-01",{"date":44,"type":21},"2028-02-28",{"name":46,"class":47},"CHA University","OTHER",12,{"id":50,"slug":51,"hasResults":11,"nctId":52,"briefTitle":53,"officialTitle":54,"acronym":55,"eligibilityCriteria":56,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":57,"targetDuration":4,"studyType":59,"phases":4,"briefSummary":60,"conditions":61,"keywords":65,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":74,"lastUpdatePostDateStruct":75,"startDateStruct":77,"completionDateStruct":79,"leadSponsor":81,"locationsCount":83},"100639433","diagnostic-accuracy-of-gpt-4o-and-claude-for-heart-score-calculation-in-chest-pain-100639433","NCT07626060","Diagnostic Accuracy of GPT-4o and Claude for HEART Score Calculation in Chest Pain","Diagnostic Accuracy of Large Language Models (GPT-4o and Claude) in HEART Score Calculation and 30-Day MACE Prediction in Emergency Department Chest Pain Patients: A Prospective Observational Validation Study Against Three-Expert Consensus","LLM-HEART","INCLUSION CRITERIA:\n\n* Age \\>=18 years\n* Chief complaint of non-traumatic chest pain at the emergency department\n* Written informed consent obtained from the patient or legally authorized representative\n* Availability for 30-day follow-up (reachable by telephone and\u002For actively registered in the e-Nabiz national health database)\n\nEXCLUSION CRITERIA:\n\n* Traumatic chest pain etiology\n* ST-elevation myocardial infarction (STEMI) at presentation requiring immediate reperfusion protocol\n* Refusal or subsequent withdrawal of informed consent\n* Inability to complete the mandatory 30-day follow-up period\n\nWITHDRAWAL CRITERIA:\n\n* Patient or representative requests data withdrawal after initial consent\n* Administrative identification of retrospective data entry after enrollment",{"count":58,"type":21},690,"OBSERVATIONAL","This prospective observational diagnostic accuracy study evaluates whether large language models (LLMs) - GPT-4o (OpenAI, gpt-4o-2024-11-20) and Claude (Anthropic, claude-sonnet-4-6) - can accurately calculate HEART scores from unstructured Turkish clinical notes and predict 30-day major adverse cardiac events (MACE) in emergency department patients presenting with non-traumatic chest pain.\n\nThe study will enroll 600 consecutive adult patients. For each patient, the same anonymized data (free-text anamnesis, ECG report text, troponin value, and age) will be independently processed by both LLMs via separate API calls with deterministic settings (temperature=0, JSON format). A three-expert consensus HEART score - derived through blinded independent scoring by three emergency medicine physicians with majority-vote adjudication - serves as the reference standard for agreement analysis. Actual 30-day MACE (all-cause death, AMI Type 1\u002F2\u002F4b, unplanned revascularization) determined via national health database and telephone follow-up serves as the outcome for diagnostic accuracy analysis.\n\nA secondary documentation-quality sub-study will quantify how spontaneously Turkish emergency anamnesis notes capture HEART score parameters.",[62,63,64,28],"Emergency Medicine","Artificial Intelligence (AI)","Artificial Intelligence (AI) in Diagnosis",[66,67,68,69,70,71,72,73],"Large Language Model","GPT-4o","Claude Sonnet","Emergency Department","Diagnostic Accuracy","Medical Informatics","Physician vs AI","HEART score","2026-06-22",{"date":76,"type":40},"2026-06-23",{"date":78,"type":21},"2026-06",{"date":80,"type":21},"2027-06",{"name":82,"class":47},"Marmara University Pendik Training and Research Hospital",1]