Development and Prospective Validation of an AI-Based Diagnostic Model for Hepato-Pancreato-Biliary Diseases | NCT07716670

NCT07716670

Overview

NCT ID: NCT07716670
Lead Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang UniversityOTHER
Official Title: Development and Prospective Validation of an AI-Based Diagnostic Model for Hepato-Pancreato-Biliary Diseases
Brief 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.

Status and Timeline

Status: Not yet recruiting
Last Update Posted: Jul 21, 2026Actual
Start Date: Aug 15, 2026Estimated
Primary Completion Date: Nov 30, 2026Estimated
Completion Date

Participation Criteria

Ages Eligible for Study: Adult (18-64)Older Adult (65+)
Sexes Eligible for Study: All
Accepts Healthy Volunteers?: Yes
Eligibility Criteria: 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 <24) or language barriers preventing effective interaction with the AI system.

Psychiatric disorders or altered mental status affecting decision-making capacity.

Prior or Concurrent Participation:

Enrollment in other interventional clinical trials that may confound the study outcomes.

Current use of experimental diagnostic tools or AI systems outside the study protocol.

Technical or Logistical Constraints:

Inability to access or operate electronic devices required for AI system interaction (e.g., touchscreen terminals, mobile apps).

Lack of stable internet connectivity for system access.

Ethical or Legal Restrictions: Pregnancy or lactation (to avoid potential risks not directly related to the study).

Study Plan

Study Type: Observational
Phase: No data available
Enrollment: 400Estimated
Primary Purpose: No data available
Allocation: No data available
Interventional Model: No data available
Masking: No data available

Contacts and Locations

Central Contacts: ding yuan, doctorContact
dingyuan@zju.edu.cn18858101960

Similar Studies

Participant Group/Arms

Experimental Group 1

Patients in this group will first complete a full interaction with the multi-agent system described above until the Arbiter confirms the medical record is error-free. The system will then generate a structured "Case Characteristics" summary and an initial diagnostic recommendation produced by the Oracle. However, this complete AI-generated output will not be displayed to the subsequent attending physician. The physician will then conduct an independent routine consultation following standard clinical protocols. The medical records generated by the physician are solely for maintaining the integrity of clinical workflows and will not be used as evaluation metrics for this study. The core assessment objective for this group is to evaluate the concordance between the AI-generated final medical records and the predefined gold standard.

Other: AI Integration type 1

Experimental Group 2

Patients in this group will complete the interaction with the multi-agent system and confirm the final "Case Characteristics" (CC). The system will then push this structured CC summary (excluding the Oracle's diagnostic recommendations to avoid excessive guidance) to the attending physician's electronic workstation in a standardized format. Prior to the formal consultation, physicians may refer to this summary to adjust their interview priorities, verify information accuracy, or supplement missing details. The medical records written by the physicians will serve as the primary evaluation metrics for this group.

Other: AI Integration type 2

Control Group

This group will exclude AI intervention entirely. Physicians will independently complete the consultation and documentation from scratch, serving as the baseline reference for evaluating AI system performance.