[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"hepatic-disease\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:hepatic-disease":25},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,2,0,[8,46],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":13,"acronym":4,"eligibilityCriteria":14,"healthyVolunteers":15,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":18,"targetDuration":21,"studyType":22,"phases":4,"briefSummary":23,"conditions":24,"keywords":29,"overallStatus":34,"whyStopped":4,"lastUpdateSubmitDate":35,"lastUpdatePostDateStruct":36,"startDateStruct":39,"completionDateStruct":41,"leadSponsor":43,"locationsCount":4},"100648018","development-and-prospective-validation-of-an-ai-based-diagnostic-model-for-hepato-pancreato-biliary-diseases-100648018",false,"NCT07716670","Development and Prospective Validation of an AI-Based Diagnostic Model for Hepato-Pancreato-Biliary Diseases","Inclusion Criteria:\n\n* Age and Gender: Patients aged 18 to 75 years, of either sex.\n* Clinical Diagnosis Requirements: Suspected or confirmed hepatobiliary or pancreatic diseases (e.g., liver cancer, pancreatic cancer, cholangiocarcinoma, cirrhosis) based on preliminary clinical evaluation.\n* Ability to provide a complete medical history and symptoms for AI system interaction.\n* Cognitive and Physical Capacity:\n* Sufficient cognitive function to complete interactions with the AI multi-agent system independently (verified by Mini-Mental State Examination \\[MMSE\\] score ≥24).\n* Proficiency in Mandarin or English to ensure accurate communication with the system.\n* Consent and Compliance: Willingness to participate and provide written informed consent.\n* Ability to complete all study procedures, including physician consultations and follow-up assessments.\n* Clinical Workflow Compatibility: Scheduled for outpatient consultation at participating healthcare facilities.\n\nExclusion Criteria:\n\n* Patients with life-threatening conditions requiring immediate intervention (e.g., acute hepatic failure, severe hemorrhage).\n* Presence of severe cardiovascular or cerebrovascular diseases that may interfere with study participation.\n* Cognitive or Communication Barriers:\n* Cognitive impairment (MMSE score \\\u003C24) or language barriers preventing effective interaction with the AI system.\n* Psychiatric disorders or altered mental status affecting decision-making capacity.\n* Prior or Concurrent Participation:\n* Enrollment in other interventional clinical trials that may confound the study outcomes.\n* Current use of experimental diagnostic tools or AI systems outside the study protocol.\n* Technical or Logistical Constraints:\n* Inability to access or operate electronic devices required for AI system interaction (e.g., touchscreen terminals, mobile apps).\n* Lack of stable internet connectivity for system access.\n* Ethical or Legal Restrictions: Pregnancy or lactation (to avoid potential risks not directly related to the study).",true,"ALL","18 Years",{"count":19,"type":20},400,"ESTIMATED","4 Weeks","OBSERVATIONAL","The rapid advancement of artificial intelligence (AI) has expanded its applications in healthcare, particularly in diagnostic assistance, intelligent triage, and patient interaction. Hepatobiliary and pancreatic diseases (such as liver cancer, pancreatic cancer, cirrhosis) are characterized by insidious onset, rapid progression, low early-diagnosis rates, and poor prognosis. However, grassroots medical institutions in China face challenges including physician shortages, variable patient health literacy, and incomplete initial information collection, leading to high misdiagnosis\u002Fmissed diagnosis risks.\n\nRecent breakthroughs in large language models (LLMs) and multi-agent systems (MAS) offer new solutions. LLMs enable advanced natural language processing, while MAS coordinates specialized agents for complex decision-making. Integrating MAS with medical LLMs could create intelligent pre-consultation systems that systematically collect patient symptoms, risk factors, family history, and lifestyle data to enhance diagnostic efficiency.\n\nThis study aims to develop a MAS-based pre-consultation system for hepatobiliary-pancreatic diseases featuring four specialized agents (\"guidance agent,\" \"medical history agent,\" \"risk assessment agent,\" and \"summary generation agent\"). The system will simulate clinical reasoning to generate structured diagnostic reports for physicians.\n\nResearch Objectives:\n\nDevelop a specialized multi-agent framework combining LLMs to simulate clinical diagnostic logic and standardize symptom collection Enhance pre-consultation data integrity through intelligent dialogue focusing on key disease indicators Generate structured diagnostic summaries highlighting critical symptoms and risk factors Establish foundation for clinical validation and application through expert evaluation and user feedback This pre-diagnostic tool will assist physicians rather than replace clinical judgment, promoting safe, effective AI applications in early disease screening and tiered healthcare systems.",[25,26,27,28],"Hepatic Disease","Biliary Disease","Pancreas Disease","Artificial Intelligence (AI) in Diagnosis",[30,31,32,33],"hepatic disease","biliary disease","pancreas disease","artificial intelligence in diagnosis","NOT_YET_RECRUITING","2026-07-15",{"date":37,"type":38},"2026-07-21","ACTUAL",{"date":40,"type":20},"2026-08-15",{"date":42,"type":20},"2026-11-30",{"name":44,"class":45},"Second Affiliated Hospital, School of Medicine, Zhejiang University","OTHER",{"id":47,"slug":48,"hasResults":11,"nctId":49,"briefTitle":50,"officialTitle":50,"acronym":51,"eligibilityCriteria":52,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":53,"targetDuration":4,"studyType":55,"phases":56,"briefSummary":58,"conditions":59,"keywords":4,"overallStatus":34,"whyStopped":4,"lastUpdateSubmitDate":63,"lastUpdatePostDateStruct":64,"startDateStruct":66,"completionDateStruct":68,"leadSponsor":70,"locationsCount":72},"100527871","phase-3-prolonged-hypercoagulability-following-major-liver-resection-for-malignancy-100527871","NCT06153394","Prolonged Hypercoagulability Following Major Liver Resection for Malignancy","PRIORITY","Inclusion Criteria:\n\n1. Adults aged 18 years or older at the time of enrollment.\n2. Requiring major liver resection (\\>2 liver sections) for any oncologic indication.\n3. Requiring postoperative thromboprophylaxis will be included.\n4. Willing and able to perform subcutaneous injections according to the study protocol, or receive injections form a caregiver delegated by the participant.\n\nExclusion Criteria:\n\n1. Anyone below 18 years of age.\n2. Patients on current anticoagulant and\u002For antiplatelet therapy\n3. Patients with a history of thrombotic events\n4. Patients with a coagulation disorder.\n5. Patients with recognized thrombophilia.\n6. Patients who cannot understand\u002Fspeak or read in English.",{"count":54,"type":20},50,"INTERVENTIONAL",[57],"PHASE3","This clinical trial will investigate the ability of thromboelastrogrpahy (TEG®) to detect hypercoagulability after liver surgery and will examine the effect of extended thromboprophylaxis (medical treatment to prevent the development of blood clots inside blood vessels) in patients undergoing liver surgery for cancer treatment.\n\nThe liver plays a key role in regulating the process of blood clotting. As a result, blood clots are a major cause of complications and death following liver surgery. This is especially true in cancer patients who are at a higher risk of developing blood clots. Current methods for preventing clotting complications after liver surgery include conventional coagulation blood tests (CCTs) and anticoagulant drugs, such as low molecular weight heparins (LMWHs). Current LMWH treatment is prescribed for one month after surgery, but studies show that the risk of developing blood clots can last up to 3 months. Studies also show that CCTs may not be as effective in detecting clotting issues as more comprehensive testing systems, such as TEG. This study will randomize 50 participants to receive 90 days of thromboprophylaxis (using the LMWH Redesca) or the standard of care 30 days (using the LMWH Fragmin) after liver surgery. The medication will be given by injection, similar to a regular vaccine or an insulin injection. Participants will inject the medication every day, for 30 or 90 days, after surgery. Participants will also have their blood tested for clotting issues via TEG testing before surgery and on post-operative days 1,3,5,30 and 90. After surgery, participants will be monitored by their surgeon for clotting complications and 3 year disease-free survival.",[25,60,61,62],"Surgery-Complications","Hypercoagulability","Thrombosis","2024-04-22",{"date":65,"type":38},"2024-04-23",{"date":67,"type":20},"2024-06-01",{"date":69,"type":20},"2028-02-01",{"name":71,"class":45},"Western University, Canada",1]