[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100635616":3},{"organization":4,"armGroups":7,"interventions":15,"overallOfficials":25,"centralContacts":29,"locations":35,"responsibleParty":51,"collaborators":10,"id":54,"slug":55,"hasResults":56,"nctId":57,"briefTitle":58,"officialTitle":59,"acronym":10,"eligibilityCriteria":60,"healthyVolunteers":61,"sex":62,"minAge":63,"maxAge":10,"enrollmentInfo":64,"targetDuration":10,"studyType":67,"phases":10,"briefSummary":68,"conditions":69,"keywords":73,"overallStatus":37,"whyStopped":10,"lastUpdateSubmitDate":79,"lastUpdatePostDateStruct":80,"startDateStruct":83,"completionDateStruct":85,"leadSponsor":87,"locationsCount":88},{"fullName":5,"class":6},"The Third Affiliated Hospital of Southern Medical University","OTHER_GOV",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"Validation Cohort",null,"A retrospective dataset of medical imaging cases (including CT and MRI) collected from multiple centers, representing common systemic diseases, used to evaluate the diagnostic performance of the multimodal large model.",[13,14],"Other: Standalone Radiologist Interpretation","Other: AI-assisted Radiologist Interpretation",[16,21],{"type":17,"name":18,"description":19,"armGroupLabels":20,"otherNames":10},"OTHER","Standalone Radiologist Interpretation","Radiologists interpret the medical images independently without any assistance from the AI model to establish a baseline performance.",[9],{"type":17,"name":22,"description":23,"armGroupLabels":24,"otherNames":10},"AI-assisted Radiologist Interpretation","Radiologists interpret the same set of medical images with the assistance of the multimodal medical imaging large model to evaluate the improvement in diagnostic performance.",[9],[26],{"name":27,"affiliation":5,"role":28},"Yinghua Zhao, PhD","PRINCIPAL_INVESTIGATOR",[30],{"name":31,"role":32,"phone":33,"phoneExt":10,"email":34},"Tao Li, MD","CONTACT","+86-15527360835","lt12420131@163.com",[36],{"facility":5,"status":37,"city":38,"state":39,"zip":40,"country":41,"countryCode":42,"cosmosGeoPoint":43,"geoPoint":48,"contacts":49},"RECRUITING","Guangzhou","Guangdong","510630","China","CN",{"type":44,"coordinates":45},"Point",[46,47],113.25,23.11667,{"lat":47,"lon":46},[50],{"name":31,"role":32,"phone":33,"phoneExt":10,"email":34},{"type":28,"investigatorFullName":52,"investigatorTitle":53,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"Junyan Li","Scientific Research Administrator","100635616","prospective-user-study-and-multicenter-validation-of-multimodal-medical-imaging-large-models-100635616",false,"NCT07555002","Prospective User Study and Multicenter Validation of Multimodal Medical Imaging Large Models","Prospective User Study and Multicenter Validation of Multimodal Medical Imaging Large Models in the Diagnosis of Common Systemic Diseases","Inclusion Criteria:\n\n* Patients who underwent systemic medical imaging examinations (e.g., CT or MRI) at participating centers for common systemic diseases.\n* Imaging data must have confirmed clinical reference standards, expert consensus, or pathological diagnosis.\n* Availability of complete DICOM format images with standard acquisition protocols.\n\nExclusion Criteria:\n\n* Poor image quality (e.g., severe motion or metal artifacts) that precludes definitive diagnosis.\n* Cases with incomplete clinical or pathological reference standards.\n* Corrupted image files or duplicate cases.",true,"ALL","18 Years",{"count":65,"type":66},1000,"ESTIMATED","OBSERVATIONAL","This study aims to evaluate the diagnostic performance and clinical utility of a multimodal medical imaging large model in identifying common systemic diseases. Through a retrospective reader study involving multiple centers, the research will compare the diagnostic accuracy, sensitivity, and specificity of radiologists with and without AI assistance. The goal is to validate the model's robustness and its impact on the diagnostic efficiency of clinicians across diverse healthcare settings.",[70,71,72],"Diagnostic Imaging","Common Systemic Diseases","Artificial Intelligence (AI)",[74,75,76,77,78],"Multimodal Large Model","Deep Learning","Radiology","Multicenter Study","Diagnostic Performance","2026-05-05",{"date":81,"type":82},"2026-05-08","ACTUAL",{"date":84,"type":82},"2026-01-01",{"date":86,"type":66},"2026-12",{"name":5,"class":6},1]