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

Summary

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.

Conditions

• Hepatic Disease
• Biliary Disease
• Pancreas Disease
• Artificial Intelligence (AI) in Diagnosis

Interventions

OTHER

AI Integration type 1

Patients first complete a full interaction with the multi-agent system until the Arbiter confirms the medical record is error-free. The system then generates a structured "Case Characteristics" (CC) summary and preliminary diagnostic recommendations via the Oracle Agent. However, the complete AI output is not displayed to the subsequent attending physician. The physician conducts an independent consultation following standard clinical protocols, and their medical records are solely used to maintain clinical workflow integrity and are not evaluated as part of this study. The core assessment objective for this group is the concordance between the AI-generated final medical records and the gold-standard reference.

OTHER

AI Integration type 2

After patients finalize the CC through interaction with the multi-agent system, the structured "Case Characteristics" (excluding Oracle-generated diagnostic advice to avoid over-guidance) are pushed in a standardized format to the corresponding physician's electronic workstation. Physicians may reference this summary before formal consultation to adjust their questioning focus, verify information accuracy, or supplement missing details. The physician's final written medical record serves as the primary evaluation object for this group.

Sponsors & Collaborators

• Second Affiliated Hospital, Zhejiang University, School of Medicine

  lead OTHER

Principal Investigators

• ding yuan, doctor · Second Affiliated Hospital, Zhejiang University, School of Medicine

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2026-08-15

Primary Completion

2026-11-30

Completion

2026-11-30