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.
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.
Sufficient cognitive function to complete interactions with the AI multi-agent system independently (verified by Mini-Mental State Examination [MMSE] score ≥24).
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 impairment (MMSE score <24) or language barriers preventing effective interaction with the AI system.
Psychiatric disorders or altered mental status affecting decision-making capacity.
The study population will consist of adult outpatients (aged 18-75 years) with suspected or confirmed hepatobiliary and pancreatic diseases (e.g., liver cancer, pancreatic cancer, cholangiocarcinoma, cirrhosis) who are scheduled for routine outpatient consultations at participating healthcare institutions. Participants must demonstrate sufficient cognitive function (Mini-Mental State Examination \[MMSE\] score ≥24) and proficiency in Mandarin or English to ensure effective interaction with the AI multi-agent system. All individuals must provide written informed consent and be capable of completing all study procedures, including structured interactions with the AI system and follow-up physician evaluations.
Cohort
Prospective
AI Integration type 1
Other interventionPatients 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.
AI Integration type 2
Other interventionAfter 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.
Concordance Rate Between AI-Generated Medical Records and Gold Standard
Proportion of AI-generated "Case Characteristics" summaries that match the gold standard (established by expert panels) in key diagnostic elements (e.g., symptom description, risk factors, physical findings).
Diagnostic Accuracy of Physician Documentation With AI Summary Reference
Proportion of physician-completed medical records in Experimental Group 2 that meet predefined quality criteria (completeness, diagnostic relevance, alignment with gold standard).
Proportion of physician medical records meeting predefined quality criteria without AI support
Proportion of physician-completed medical records that meet predefined quality criteria (completeness, diagnostic relevance, alignment with gold standard) in the absence of AI assistance.
Average physician consultation duration
Comparison of average consultation times across study groups.
Average pre-consultation preparation time for physicians
Time spent by physicians reviewing AI-generated case summaries before consultation.
Patient satisfaction measured by validated questionnaires
Patient-reported experience measured via validated satisfaction questionnaires. Scores range from 0 to 100; higher scores indicate greater satisfaction, assessing ease of AI interaction and perceived care quality.
Physician satisfaction with AI workflow
Physician survey evaluating perceived efficiency, usability, and clinical utility of AI tools. Survey scores range from 0 to 100; higher survey scores represent better satisfaction with the AI system.
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