With the rapid development of artificial intelligence (AI) technologies, their applications in the healthcare field have become increasingly widespread, particularly demonstrating significant potential in diagnostic assistance, intelligent triage, and patient interaction. Hepatobiliary and pancreatic diseases (e.g., liver cancer, pancreatic cancer, bile duct cancer, cirrhosis) are characterized by insidious onset, rapid progression, low early diagnostic rates, and poor prognosis. Clinical diagnosis and treatment demand high requirements for early recognition and precise evaluation. However, grassroots medical institutions in China commonly face challenges such as insufficient specialist physicians, heterogeneous patient health literacy, and inadequate initial information collection during first visits, leading to a high risk of missed or incorrect diagnoses and delays in optimal intervention timing.
In recent years, large language models (LLMs) have achieved breakthrough advancements in natural language understanding and generation, providing a technical foundation for constructing intelligent, personalized medical dialogue systems. Multi-agent systems (MAS) simulate collaborative workflows among multiple specialized roles, enabling more complex and structured decision-making processes with notable advantages in task decomposition, knowledge integration, and dynamic reasoning. Integrating multi-agent architectures with medical LLMs offers the potential to develop intelligent pre-consultation systems with domain-specific expertise, enabling systematic collection and preliminary analysis of critical patient information-including symptoms, risk factors, family history, and lifestyle-to enhance the efficiency and completeness of medical consultations.
This study aims to develop an intelligent pre-consultation LLM system for hepatobiliary and pancreatic diseases based on a multi-agent architecture. By establishing clearly defined "guidance agents," "medical history collection agents," "risk assessment agents," and "physician summary generation agents," the system will simulate clinical diagnostic logic, proactively guide patients through structured disease presentations, and automatically generate initial diagnostic reports compliant with clinical standards for physician reference. This system seeks to assist grassroots physicians in improving early recognition of hepatobiliary and pancreatic diseases, optimize outpatient resource allocation, and enhance patient healthcare experiences.
Research Objectives:
This study aims to design, develop, and preliminarily validate an intelligent pre-consultation LLM system for hepatobiliary and pancreatic diseases based on a multi-agent architecture, exploring its feasibility and potential value in clinical pre-consultation scenarios. Specific objectives include:
Establishing a specialized multi-agent collaborative framework: Leveraging LLM technology, design a multi-agent system capable of division of labor and collaboration, incorporating modules such as guidance, medical history collection, preliminary risk screening, and report generation. By simulating the diagnostic logic and reasoning pathways of clinical physicians, the system will achieve systematic, structured information collection for hepatobiliary and pancreatic disease-related symptoms.
Enhancing the completeness and accuracy of pre-consultation information: Through intelligent dialogue, proactively guide patients to review and articulate their medical history, prioritizing key risk factors for hepatobiliary and pancreatic diseases-including jaundice, abdominal pain, weight loss, alcohol consumption history, history of viral hepatitis, and family history of cancer-to improve patient self-reporting completeness and reduce information omissions caused by memory biases or unclear expression.
Generating structured initial diagnostic assistance reports: Automatically integrate patient-reported symptoms and medical history into pre-consultation summaries adhering to clinical documentation standards, highlighting critical physical signs and risk alerts. This will assist physicians in rapidly grasping patient profiles, shortening outpatient consultation times, and improving diagnostic efficiency.
Laying the foundation for subsequent clinical validation and application: Through small-scale simulation testing and expert evaluation, preliminarily verify the system's usability and clinical alignment, collect feedback from physicians and patients, and optimize interactive workflows. This will provide scientific evidence and ethical compliance support for future prospective clinical studies and product development.
This research is not intended for direct clinical diagnosis or treatment decisions but rather as an information-assistance tool for physicians prior to consultations. It aims to promote safe, effective, and equitable applications of AI in the early screening of hepatobiliary and pancreatic diseases and tiered healthcare delivery systems.
