[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"natural-language-processing-nlp\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:natural-language-processing-nlp":28},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,2,0,[8,43],{"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":4,"overallStatus":30,"whyStopped":4,"lastUpdateSubmitDate":31,"lastUpdatePostDateStruct":32,"startDateStruct":35,"completionDateStruct":37,"leadSponsor":39,"locationsCount":42},"100644831","large-language-models-for-dental-radiology-report-generation-from-structured-textual-data-100644831",false,"NCT07676318","Large Language Models for Dental Radiology Report Generation From Structured Textual Data","Evaluation of Large Language Models for Transforming Structured Dental Radiology Data Into Narrative Radiology Reports","DENT-LLM","Inclusion Criteria:\n\n* Dental radiology records based on dental X-ray examination performed on the basis of a written referral from a dentist or physician\n* Dental X-ray examinations performed for screening, diagnostic, or treatment-planning purposes\n* Records from patients with permanent dentition after completion of exfoliation\n\nExclusion Criteria:\n\n* Records from patients with mixed dentition before completion of exfoliation\n* Records with incomplete, ambiguous, or internally inconsistent structured dental radiology data preventing reliable report generation\n* Records with missing information required for evaluation of the generated report\n* Duplicate records from the same radiographic examination\n* Records in which anonymization or pseudonymization cannot be ensured","ALL","18 Years",{"count":20,"type":21},100,"ESTIMATED","OBSERVATIONAL","The purpose of this observational methodological study is to evaluate whether large language models can transform structured dental radiology data into clear narrative radiology reports. Large language models are computer programs that can generate text from information provided to them. In this study, the input will consist of organized dental radiology findings, such as chart-style or diagram-based information about teeth and surrounding structures.\n\nDental radiology reports are used by dentists and other health care providers to understand imaging findings and support clinical documentation. Preparing narrative reports may be time-consuming, and the wording of reports may vary between clinicians. This study will examine whether language-model-assisted report generation can produce reports that are complete, accurate, understandable, and clinically useful.\n\nThe study will compare reports generated with support from large language models with traditionally prepared reports. Researchers will also assess how the wording of the prompt and selected model parameters influence report quality. In addition, the study will analyze errors and safety risks in generated reports and evaluate whether such a system could be practical in a dental radiology workflow. The language model will not make treatment decisions, and generated reports will be used for research evaluation only.",[25,26,27,28,29],"Radiography","Oral Health","Large Language Model","Natural Language Processing (NLP)","Dentistry","NOT_YET_RECRUITING","2026-06-24",{"date":33,"type":34},"2026-06-30","ACTUAL",{"date":36,"type":21},"2026-06-29",{"date":38,"type":21},"2026-07-26",{"name":40,"class":41},"Hospital of the Ministry of Interior, Kielce, Poland","OTHER",1,{"id":44,"slug":45,"hasResults":11,"nctId":46,"briefTitle":47,"officialTitle":47,"acronym":4,"eligibilityCriteria":48,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":49,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":51,"conditions":52,"keywords":4,"overallStatus":30,"whyStopped":4,"lastUpdateSubmitDate":57,"lastUpdatePostDateStruct":58,"startDateStruct":60,"completionDateStruct":62,"leadSponsor":64,"locationsCount":42},"100639884","research-on-the-whole-process-of-ai-intelligent-management-system-for-the-diagnosis-and-treatment-of-inflammatory-bowel-diseases-100639884","NCT07590271","Research on the Whole Process of AI Intelligent Management System for the Diagnosis and Treatment of Inflammatory Bowel Diseases","Inclusion Criteria:\n\nInclusion criteria for the IBD group:\n\nPatients diagnosed with IBD (K50.00 to K51.919) within the specified time interval.\n\nInclusion criteria for the non-IBD group:\n\nPatients never diagnosed with IBD (K50.00 to K51.919) within the specified time interval.\n\nExclusion Criteria:\n\nPatients not within the specified time interval Deceased patients\n\nExclusion criteria for the non-IBD group:\n\nPatients not within the specified time interval Deceased patients Patients with fewer than 5 hospital visits",{"count":50,"type":21},4500,"Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is a chronic immune-mediated disorder requiring long-term management. Clinically, IBD may involve recurrent intestinal inflammation, ulcer formation, and complications such as strictures and fistulas. The etiology of IBD is associated with immune dysregulation, gut microbiome imbalance, and genetic susceptibility. Its clinical manifestations are heterogeneous; early symptoms such as abdominal pain, diarrhea, weight loss, hematochezia, or anemia often resemble gastroenteritis, irritable bowel syndrome, or infectious enterocolitis, leading to misdiagnosis and delayed diagnosis. According to international studies, the interval between initial symptom onset and confirmed diagnosis can range from several months to years, during which untreated disease progression increases the risks of hospitalization, surgery, bowel strictures, and fistulizing complications, resulting in significant impacts on patient quality of life.\n\nThis study adopts a retrospective design, analyzing our hospital's electronic medical record data from 2023 to 2025.The objective is to evaluate the performance and feasibility of an artificial intelligence (AI) model-developed and incorporating natural language processing (NLP) and phenotypic recognition algorithms-in supporting early identification and diagnosis of IBD. The model has been validated in multiple European healthcare systems and is capable of recognizing high-risk phenotypic clusters from large-scale structured and unstructured medical data. This study represents the first application of this AI technology in the Taiwanese IBD population. All data processing will occur within a de-identified and secure computing environment to ensure data privacy and information security.\n\nThe study will compare AI-generated diagnostic suggestions derived from medical records with actual clinical diagnoses to assess consistency and accuracy. The model's performance across different clinical characteristics, disease severity levels, and stages of illness will also be examined. In addition, statistical metrics such as precision and recall will be used to generate PRC curves for determining the optimal diagnostic threshold. The outcomes of this study are expected to validate the potential of AI technology in facilitating early recognition, accelerating diagnosis, and supporting clinical decision-making for IBD. The findings will provide essential data for developing localized AI models for IBD, ultimately enhancing diagnostic efficiency, shortening the diagnostic timeline, and improving long-term patient outcomes and quality of life.\n\nObjective 1：To retrospectively analyze the clinical characteristics and diagnostic pathways of patients with IBD (CD\u002FUC).\n\nObjective 2：To evaluate the performance of the AI model in identifying and providing diagnostic suggestions for high-risk IBD cases.\n\nObjective 3：To compare the accuracy and consistency between AI-generated diagnostic suggestions and actual clinical diagnoses.",[53,54,55,56,28],"Inflammatory Bowel Disease (Crohn's Disease and Ulcerative Colitis)","Crohn's Disease (CD)","Ulcerative Colitis (UC)","Artificial Intelligence (AI)","2026-05-11",{"date":59,"type":34},"2026-05-15",{"date":61,"type":21},"2026-06-01",{"date":63,"type":21},"2027-12-31",{"name":65,"class":41},"Taichung Veterans General Hospital"]