[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100646847":3},{"organization":4,"armGroups":7,"interventions":20,"overallOfficials":26,"centralContacts":31,"locations":41,"responsibleParty":55,"collaborators":26,"id":57,"slug":58,"hasResults":59,"nctId":60,"briefTitle":61,"officialTitle":62,"acronym":63,"eligibilityCriteria":64,"healthyVolunteers":59,"sex":65,"minAge":66,"maxAge":67,"enrollmentInfo":68,"targetDuration":26,"studyType":71,"phases":72,"briefSummary":74,"conditions":75,"keywords":26,"overallStatus":77,"whyStopped":26,"lastUpdateSubmitDate":78,"lastUpdatePostDateStruct":79,"startDateStruct":82,"completionDateStruct":84,"leadSponsor":86,"locationsCount":87},{"fullName":5,"class":6},"National Taiwan University Hospital","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"AI-Assisted Prescribing","EXPERIMENTAL","Non-specialist physicians prescribe antidiabetic medications after consulting the AI-assisted antidiabetic drug consultation system. The system provides real-time, interactive prescribing recommendations, a drug-prioritization order, and outcome predictions. The physician retains full control over the final prescribing decision. All medications are approved in Taiwan and prescribed within approved dose ranges. Patients are followed for 12 months.",[13],"Device: AI-assisted antidiabetic drug consultation system",{"label":15,"type":16,"description":17,"interventionNames":18},"Manual Prescribing (Non-AI)","ACTIVE_COMPARATOR","Non-specialist physicians prescribe antidiabetic medications manually according to usual clinical practice, without using the AI consultation system. All medications are approved in Taiwan and prescribed within approved dose ranges. Patients are followed for 12 months.",[19],"Other: Manual antidiabetic prescribing (without AI)",[21,27],{"type":22,"name":23,"description":24,"armGroupLabels":25,"otherNames":26},"DEVICE","AI-assisted antidiabetic drug consultation system","A machine-learning based clinical decision support software that provides non-specialist physicians with real-time, interactive antidiabetic prescribing recommendations, a drug-prioritization order, and outcome predictions (e.g., the predicted likelihood of reaching glycemic targets and responder\u002Fnon-responder status for individual drugs). The system was developed and validated using the NTUH integrated medical database platform. It provides advisory recommendations only; the treating physician retains full control over the final prescribing decision. All recommended medications are approved in Taiwan and within approved dose ranges.",[9],null,{"type":6,"name":28,"description":29,"armGroupLabels":30,"otherNames":26},"Manual antidiabetic prescribing (without AI)","Antidiabetic medications prescribed manually by non-specialist physicians according to usual clinical practice, without using the AI consultation system. All medications are approved in Taiwan and prescribed within approved dose ranges.",[15],[32,37],{"name":33,"role":34,"phone":35,"phoneExt":26,"email":36},"Yi-Cheng Chang, M.D.","CONTACT","886+0972651027","b83401040@gmail.com",{"name":38,"role":34,"phone":39,"phoneExt":26,"email":40},"Pan Hou Che, PhD student","886+0987197997","d12456001@g.ntu.edu.tw",[42],{"facility":5,"status":26,"city":43,"state":26,"zip":44,"country":45,"countryCode":46,"cosmosGeoPoint":47,"geoPoint":52,"contacts":53},"Taipei","100","Taiwan","TW",{"type":48,"coordinates":49},"Point",[50,51],121.52639,25.05306,{"lat":51,"lon":50},[54],{"name":33,"role":34,"phone":35,"phoneExt":26,"email":36},{"type":56,"investigatorFullName":26,"investigatorTitle":26,"investigatorAffiliation":26,"oldNameTitle":26,"oldOrganization":26},"SPONSOR","100646847","ai-assisted-antidiabetic-drug-consultation-system-for-glycemic-control-in-type-2-diabetes-patients-managed-by-non-specialist-physicians-100646847",false,"NCT07684690","AI-Assisted Antidiabetic Drug Consultation System for Glycemic Control in Type 2 Diabetes Patients Managed by Non-Specialist Physicians","Clinical Validation of AI-assisted Antidiabetic Drug Consultation System-1","AI-ADCS","Inclusion Criteria:\n\n* Adults aged 18 to 80 years\n* Diagnosis of type 2 diabetes for at least 6 months\n* HbA1c above 8% within the past 3 months\n* Currently using one or more oral antidiabetic drugs\n* Able to understand and provide written informed consent\n\nExclusion Criteria:\n\n* Pregnancy or breastfeeding\n* Recent participation in another interventional clinical trial\n* Cognitive impairment precluding understanding of the study\n* Active cancer treatment within the past 6 years\n* Use of systemic steroids","ALL","19 Years","80 Years",{"count":69,"type":70},400,"ESTIMATED","INTERVENTIONAL",[73],"NA","This study tests whether an artificial intelligence (AI) tool can help doctors choose better diabetes medicines for their patients. Type 2 diabetes is very common, but there are far more patients than diabetes specialists, so many patients are treated by doctors who are not diabetes specialists. The researchers built an AI consultation system that gives doctors real-time suggestions and predictions about diabetes medicines while they are prescribing. The doctor always makes the final decision.\n\nIn this trial, patients with type 2 diabetes whose blood sugar is not well controlled will be placed by chance (randomly) into one of two groups. In one group, the doctor uses the AI system when deciding on diabetes medicines. In the other group, the doctor prescribes as usual, without the AI system. All medicines used are already approved in Taiwan and given at approved doses.\n\nThe study follows each patient for 12 months, with check-ups at the start and at 3, 6, 9, and 12 months. The main goal is to compare how much the patients' long-term blood sugar level (HbA1c) improves between the two groups after one year. The researchers also look at how many patients reach their blood sugar target, how often low blood sugar happens, and whether any side effects occur. The aim is to find out whether using the AI tool leads to better blood sugar control.",[76],"Type 2 Diabetes Mellitus (T2DM)","NOT_YET_RECRUITING","2026-06-29",{"date":80,"type":81},"2026-07-06","ACTUAL",{"date":83,"type":70},"2026-07",{"date":85,"type":70},"2027-11",{"name":5,"class":6},1]