[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"large-language-models\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:large-language-models":34},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,3,0,[8,64,92],{"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":4,"briefSummary":23,"conditions":24,"keywords":37,"overallStatus":51,"whyStopped":4,"lastUpdateSubmitDate":52,"lastUpdatePostDateStruct":53,"startDateStruct":56,"completionDateStruct":58,"leadSponsor":60,"locationsCount":63},"100649713","benchmarking-large-language-models-against-tumour-boards-for-oncology-treatment-recommendations-100649713",false,"NCT07739121","Benchmarking Large Language Models Against Tumour Boards for Oncology Treatment Recommendations","Benchmarking AI for Clinical Oncology decisioNmaking (BEACON): A Prospective, Multicentre, Blinded Evaluation of Frontier Large Language Models Against Multidisciplinary Tumour Board Recommendations in Oncology Treatment Planning","BEACON","Inclusion Criteria:\n\n* Synthetic oncology case within one of the five predefined localisations (breast, lung, urological, digestive, gynaecological).\n* Complete structured schema: UICC 8th-edition stage, biomarkers, ECOG performance status, comorbidities and a standardised clinical question.\n* A clinically answerable treatment-planning question that is mappable to the locked guideline matrix.\n\nExclusion Criteria:\n\n* Case outside the five predefined localisations.\n* Incomplete, internally inconsistent or ambiguous schema.\n* Duplicate or near-duplicate of an existing case in the set.\n* Question not resolvable by current guidelines.","ALL","18 Years",{"count":20,"type":21},100,"ESTIMATED","OBSERVATIONAL","BEACON (Benchmarking AI for Clinical Oncology decisioNmaking) is a prospective, multicentre, comparative, blinded, non-interventional benchmark evaluating the treatment recommendations of five frontier large language models (LLMs) against the recommendations of multidisciplinary tumour boards (RCP) in oncology treatment planning. One hundred standardised synthetic cases (20 per localisation, across breast, lung, urological, digestive and gynaecological cancers) are submitted as identical structured input to two independent tumour boards per localisation and to five frontier LLMs. Each recommendation - human or model - is decomposed into five predefined decision domains (intent, surgery, radiotherapy, systemic therapy, work-up and biomarkers) and scored 0\u002F1\u002F2 for concordance against a two-tier reference: the consensus of the two tumour boards, complemented by an a priori locked guideline matrix (ESMO, NCCN). The primary endpoint is domain-level concordance between LLM and RCP consensus, expressed as a linearly weighted Cohen's kappa. A co-primary safety endpoint captures the proportion of recommendations carrying serious harm potential, because concordance alone can conceal dangerous errors. Because expert boards may disagree with one another on identical cases, model performance is always interpreted against the human consensus. BEACON is designed as reusable, openly licensed, pre-registered infrastructure: all synthetic cases, evaluation rubrics, the locked guideline matrix, scoring algorithms and verbatim prompts are released for full reproducibility.",[25,26,27,28,29,30,31,32,33,34,35,36],"Breast Neoplasms","Lung Neoplasms","Urologic Neoplasms","Prostatic Neoplasms","Urinary Bladder Neoplasms","Kidney Neoplasms","Digestive System Neoplasms","Genital Neoplasms","Artifical Intelligence","Large Language Models","Decision Making","Decision Support Systems, Clinical",[34,38,39,36,40,41,42,43,44,45,46,47,48,49,50],"Artificial Intelligence","Clinical Decision support","Multidisciplinary tumour board (RCP)","Patient Care Team","Medical oncology","Neoplasms","Benchmark","Benchmarking","Concordance","Weighted kappa","Synthetic data","Patient safety","Reproductibility","RECRUITING","2026-07-28",{"date":54,"type":55},"2026-07-31","ACTUAL",{"date":57,"type":55},"2026-05-01",{"date":59,"type":21},"2026-10-01",{"name":61,"class":62},"Assistance Publique - Hôpitaux de Paris","OTHER",1,{"id":65,"slug":66,"hasResults":11,"nctId":67,"briefTitle":68,"officialTitle":68,"acronym":4,"eligibilityCriteria":69,"healthyVolunteers":70,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":71,"targetDuration":4,"studyType":73,"phases":74,"briefSummary":76,"conditions":77,"keywords":79,"overallStatus":51,"whyStopped":4,"lastUpdateSubmitDate":83,"lastUpdatePostDateStruct":84,"startDateStruct":86,"completionDateStruct":88,"leadSponsor":90,"locationsCount":63},"100648874","improving-ai-assisted-medical-diagnosis-and-triage-by-the-general-public-100648874","NCT07728513","Improving AI-Assisted Medical Diagnosis and Triage by the General Public","Inclusion Criteria:\n\n* Enrolled student or employed administrative staff at LUMS.\n* 18 years of age or older.\n* Able to read and understand English.\n\nExclusion Criteria:\n\n* Individuals with any formal education or professional training in medicine, nursing, or any other healthcare profession.",true,{"count":72,"type":21},220,"INTERVENTIONAL",[75],"NA","This study is a randomized controlled trial (RCT) investigating whether access to a new LLM interface can improve medical triage and diagnostic accuracy for laypeople compared to access to a standard LLM interface. It addresses previous findings where laypeople using standard LLMs performed worse than those using conventional methods (e.g., web search) due to incomplete symptom sharing and poor interpretation of AI advice. To address this, the research tests a structured LLM system that proactively asks clinical history questions before providing a standardized, easy-to-read diagnostic output.",[78,34],"AI-Assisted Diagnosis",[80,81,82],"LLMs","Diagnosis","Triage","2026-07-22",{"date":85,"type":55},"2026-07-27",{"date":87,"type":21},"2026-07-25",{"date":89,"type":21},"2027-07-01",{"name":91,"class":62},"Lahore University of Management Sciences",{"id":93,"slug":94,"hasResults":11,"nctId":95,"briefTitle":96,"officialTitle":97,"acronym":4,"eligibilityCriteria":98,"healthyVolunteers":70,"sex":17,"minAge":99,"maxAge":100,"enrollmentInfo":101,"targetDuration":4,"studyType":73,"phases":103,"briefSummary":104,"conditions":105,"keywords":110,"overallStatus":117,"whyStopped":4,"lastUpdateSubmitDate":118,"lastUpdatePostDateStruct":119,"startDateStruct":121,"completionDateStruct":123,"leadSponsor":125,"locationsCount":63},"100623809","a-multimodal-ai-agent-for-ophthalmic-clinical-decision-support-100623809","NCT07401459","A Multimodal AI Agent for Ophthalmic Clinical Decision Support","Multicenter Randomized Controlled Trial of a Multimodal AI Agent for Ophthalmology Clinical Decision Support","Inclusion Criteria:\n\n1. Outpatient participants aged 6 to 75 years.\n2. Participants who undergo ophthalmic examinations for medical purposes during the study period.\n3. Participants who can produce clear ophthalmic images in both eyes.\n4. Agree to participate in this study with written informed consent:\n\n   1. Participants aged 18 years or older provide their own consent.\n   2. Participants aged 6-17 years require consent from a parent or legal guardian.\n\nExclusion Criteria:\n\n1. Participants who are reluctant to participate in this study.\n2. Participants presenting with acute or emergency ocular conditions requiring immediate intervention.\n3. Participants with poor quality of ophthalmic images, including blurriness, artifacts, underexposure, or overexposure.\n4. Other unsuitable reasons determined by the evaluators.","6 Years","75 Years",{"count":102,"type":21},300,[75],"This study is a multicenter randomized controlled trial evaluating the effectiveness and safety of EyeAgent, a multimodal artificial intelligence (AI) agent designed to assist ophthalmologists in clinical decision-making. Participants will be recruited from ophthalmology clinics and hospitals in Hong Kong and mainland China. The AI agent acts as a digital co-pilot, analyzing patient images and clinical history to provide diagnostic and management recommendations. The trial aims to determine whether the use of the AI agent improves diagnostic accuracy, treatment decision-making performance, report generation, workflow efficiency, and user satisfaction compared to standard clinical practice.",[106,34,107,108,109],"Ophthalmology","AI Agent","Eye Disease","Retinal Disease",[111,112,113,114,115,116],"medical AI agent","large language model","ophthalmology","tool integration","clinical decision support","real world study","NOT_YET_RECRUITING","2026-02-19",{"date":120,"type":55},"2026-02-23",{"date":122,"type":21},"2026-03-01",{"date":124,"type":21},"2026-12-31",{"name":126,"class":62},"The Hong Kong Polytechnic University"]