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

July 15, 2026 updated by: Second Affiliated Hospital, School of Medicine, Zhejiang University

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.

Study Overview

Status

Status

Indicates the current recruitment status or the expanded access status.
Not yet recruiting

Conditions

Conditions

The disease, disorder, syndrome, illness, or injury that is being studied. On ClinicalTrials.gov, conditions may also include other health-related issues, such as lifespan, quality of life, and health risks.

Intervention / Treatment

Intervention / Treatment

A process or action that is the focus of a clinical study. Interventions include drugs, medical devices, procedures, vaccines, and other products that are either investigational or already available. Interventions can also include noninvasive approaches, such as education or modifying diet and exercise.

Detailed Description

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.

Study Type

Study Type

Describes the nature of a clinical study. Study types include interventional studies (also called clinical trials), observational studies (including patient registries), and expanded access.
Observational

Enrollment (Estimated)

Enrollment

The number of participants in a clinical study. The "anticipated" enrollment is the target number of participants that the researchers need for the study.
400

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact

The name and contact information for the person who can answer enrollment questions for a clinical study. Each location where the study is being conducted may also have a specific contact, who may be better able to answer those questions.

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Eligibility Criteria

The key requirements that people who want to participate in a clinical study must meet or the characteristics they must have. Eligibility criteria consist of both inclusion criteria (which are required for a person to participate in the study) and exclusion criteria (which prevent a person from participating). Types of eligibility criteria include whether a study accepts healthy volunteers, has age or age group requirements, or is limited by sex.

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Probability Sample

Study Population

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.

Description

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

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort

Group / Cohort

A group or subgroup of participants in an observational study that is assessed for biomedical or health outcomes.
Intervention / Treatment

Intervention / Treatment

A process or action that is the focus of a clinical study. Interventions include drugs, medical devices, procedures, vaccines, and other products that are either investigational or already available. Interventions can also include noninvasive approaches, such as education or modifying diet and exercise.
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
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.
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
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.
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.

What is the study measuring?

Primary Outcome Measures

Primary Outcome Measures

In a clinical study's protocol, the planned outcome measure that is the most important for evaluating the effect of an intervention/treatment. Most clinical studies have one primary outcome measure, but some have more than one.
Outcome Measure
Measure Description
Time Frame
Concordance Rate Between AI-Generated Medical Records and Gold Standard
Time Frame: From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days
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).
From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days
Diagnostic Accuracy of Physician Documentation With AI Summary Reference
Time Frame: From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days
Proportion of physician-completed medical records in Experimental Group 2 that meet predefined quality criteria (completeness, diagnostic relevance, alignment with gold standard).
From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days
Proportion of physician medical records meeting predefined quality criteria without AI support
Time Frame: From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days
Proportion of physician-completed medical records that meet predefined quality criteria (completeness, diagnostic relevance, alignment with gold standard) in the absence of AI assistance.
From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days

Secondary Outcome Measures

Secondary Outcome Measures

In a clinical study's protocol, a planned outcome measure that is not as important as the primary outcome measure for evaluating the effect of an intervention but is still of interest. Most clinical studies have more than one secondary outcome measure.
Outcome Measure
Measure Description
Time Frame
Average physician consultation duration
Time Frame: From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days
Comparison of average consultation times across study groups.
From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days
Average pre-consultation preparation time for physicians
Time Frame: From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days
Time spent by physicians reviewing AI-generated case summaries before consultation.
From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days
Patient satisfaction measured by validated questionnaires
Time Frame: From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days
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.
From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days
Physician satisfaction with AI workflow
Time Frame: From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days
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.
From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days
Frequency of AI-related adverse events and workflow disruptions
Time Frame: From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days
Documentation of AI-related errors, workflow disruptions, or safety concerns. Count of participants experiencing safety or workflow issues.
From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days
System Usability Scale (SUS) scores for the AI interface
Time Frame: From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days
System Usability Scale (SUS) is a standardized usability questionnaire; total score ranges from 0 to 100. Higher SUS scores correspond to better perceived usability of the AI interface, completed by physicians and patients.
From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sponsor

The organization or person who initiates the study and who has authority and control over the study.
Second Affiliated Hospital, School of Medicine, Zhejiang University

Investigators

Investigators

A researcher involved in a clinical study. Related terms include site principal investigator, site sub-investigator, study chair, study director, and study principal investigator.
  • Study Chair: ding yuan, doctor, Second Affiliated Hospital, School of Medicine, Zhejiang University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

Study Start

The actual date on which the first participant was enrolled in a clinical study. The "anticipated" study start date is the date that the researchers think will be the study start date.
August 15, 2026

Primary Completion (Estimated)

Primary Completion

The date on which the last participant in a clinical study was examined or received an intervention to collect final data for the primary outcome measure. Whether the clinical study ended according to the protocol or was terminated does not affect this date. For clinical studies with more than one primary outcome measure with different completion dates, this term refers to the date on which data collection is completed for all the primary outcome measures. The "anticipated" primary completion date is the date that the researchers think will be the primary completion date for the study.
November 30, 2026

Study Completion (Estimated)

Study Completion

The date on which the last participant in a clinical study was examined or received an intervention/treatment to collect final data for the primary outcome measures, secondary outcome measures, and adverse events (that is, the last participant's last visit). The "anticipated" study completion date is the date that the researchers think will be the study completion date.
November 30, 2026

Study Registration Dates

First Submitted

First Submitted

The date on which the study sponsor or investigator first submitted a study record to ClinicalTrials.gov. There is typically a delay of a few days between the first submitted date and the record's availability on ClinicalTrials.gov (the first posted date).
March 4, 2026

First Submitted That Met QC Criteria

First Submitted That Met QC Criteria

The date on which the study sponsor or investigator first submits a study record that is consistent with National Library of Medicine (NLM) quality control (QC) review criteria. The sponsor or investigator may need to revise and submit a study record one or more times before NLM's QC review criteria are met. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
July 15, 2026

First Posted (Actual)

First Posted

The date on which the study record was first available on ClinicalTrials.gov after National Library of Medicine (NLM) quality control (QC) review has concluded. There is typically a delay of a few days between the date the study sponsor or investigator submitted the study record and the first posted date.
July 21, 2026

Study Record Updates

Last Update Posted (Actual)

Last Update Posted

The most recent date on which changes to a study record were made available on ClinicalTrials.gov. There may be a delay between when the changes were submitted to ClinicalTrials.gov by the study's sponsor or investigator (the last update submitted date) and the last update posted date.
July 21, 2026

Last Update Submitted That Met QC Criteria

Last Update Submitted That Met QC Criteria

The most recent date on which the study sponsor or investigator submitted changes to a study record that are consistent with National Library of Medicine (NLM) quality control (QC) review criteria. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
July 15, 2026

Last Verified

Last Verified

The most recent date on which the study sponsor or investigator confirmed the information about a clinical study on ClinicalTrials.gov as accurate and current. If a study with a recruitment status of recruiting; not yet recruiting; or active, not recruiting has not been confirmed within the past 2 years, the study's recruitment status is shown as unknown.
December 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

Other Study ID Numbers

Identifiers or ID numbers other than the NCT number that are assigned to a clinical study by the study's sponsor, funders, or others. These numbers may include unique identifiers from other trial registries and National Institutes of Health grant numbers.
  • 2025-1371

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Patient Privacy and Confidentiality:

IPD contains sensitive personal health information (e.g., medical history, genetic data, diagnostic results), which could risk re-identification even after anonymization. Sharing such data might violate ethical obligations under the Declaration of Helsinki and local regulations (e.g., GDPR, HIPAA).

Informed Consent Limitations:

Participants provided consent for data use within the scope of this specific study. Broad sharing of IPD for secondary purposes (e.g., unrelated research) was not explicitly authorized in the consent process, raising ethical and legal concerns.

Data Ownership and Governance:

Data may be governed by institutional or national policies restricting external access (e.g., Chinese data sovereignty laws). Sharing IPD could conflict with agreements between the study sponsor, healthcare providers, and regulatory bodies.

Risk of Misinterpretation:

Contextual or methodological details critical to interpreting IPD (e.g., AI system workfl

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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