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.

Inclusion Criteria: * Age and Gender: Patients aged 18 to 75 years, of either sex. * Clinical Diagnosis Requirements: Suspected or confirmed hepatobiliary or pancreatic diseases (e.g., liver cancer, pancreatic cancer, cholangiocarcinoma, cirrhosis) based on preliminary clinical evaluation. * Ability to provide a complete medical history and symptoms for AI system interaction. * Cognitive and Physical Capacity: * Sufficient cognitive function to complete interactions with the AI multi-agent system independently (verified by Mini-Mental State Examination \[MMSE\] score ≥24). * Proficiency in Mandarin or English to ensure accurate communication with the system. * Consent and Compliance: Willingness to participate and provide written informed consent. * Ability to complete all study procedures, including physician consultations and follow-up assessments. * Clinical Workflow Compatibility: Scheduled for outpatient consultation at participating healthcare facilities. Exclusion Criteria: * Patients with life-threatening conditions requiring immediate intervention (e.g., acute hepatic failure, severe hemorrhage). * Presence of severe cardiovascular or cerebrovascular diseases that may interfere with study participation. * Cognitive or Communication Barriers: * Cognitive impairment (MMSE score \