Clinical Research Directory

Development and Prospective Validation of an AI-Based Diagnostic Model for Hepato-Pancreato-Biliary Diseases

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/missed diagnosis risks. Recent 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. This 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. Research Objectives: Develop 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.

Prolonged Hypercoagulability Following Major Liver Resection for Malignancy

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. The 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.