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

NCT07716670 | Not Yet Recruiting DMC

• 1 other identifier: 2025-1371
• Study Type: observational
• Target: 400
• Locations: 0 countries
• Sites: N/A

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.

Conditions

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

Keywords

hepatic disease; biliary disease; pancreas disease; artificial intelligence in diagnosis

Outcome Measures

Primary Outcomes (3)

• 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).

  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

  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

  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 Outcomes (6)

• Average physician consultation duration

  From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days

• Average pre-consultation preparation time for physicians

  From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days

• Patient satisfaction measured by validated questionnaires

  From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days

• Physician satisfaction with AI workflow

  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

  From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days

Study Arms (3)

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.

Interventions

AI Integration type 1 OTHER

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 1

AI Integration type 2 OTHER

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.

Experimental Group 2

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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).

Sponsors & Collaborators

• Second Affiliated Hospital, School of Medicine, Zhejiang University: lead

Related Publications (4)

• Zhou LQ, Wang JY, Yu SY, Wu GG, Wei Q, Deng YB, Wu XL, Cui XW, Dietrich CF. Artificial intelligence in medical imaging of the liver. World J Gastroenterol. 2019 Feb 14;25(6):672-682. doi: 10.3748/wjg.v25.i6.672.

  PMID: 30783371 RESULT

• Cao LL, Peng M, Xie X, Chen GQ, Huang SY, Wang JY, Jiang F, Cui XW, Dietrich CF. Artificial intelligence in liver ultrasound. World J Gastroenterol. 2022 Jul 21;28(27):3398-3409. doi: 10.3748/wjg.v28.i27.3398.

  PMID: 36158262 RESULT

• Rompianesi G, Pegoraro F, Ceresa CD, Montalti R, Troisi RI. Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases. World J Gastroenterol. 2022 Jan 7;28(1):108-122. doi: 10.3748/wjg.v28.i1.108.

  PMID: 35125822 RESULT

• Bo Z, Song J, He Q, Chen B, Chen Z, Xie X, Shu D, Chen K, Wang Y, Chen G. Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma. Comput Biol Med. 2024 May;173:108337. doi: 10.1016/j.compbiomed.2024.108337. Epub 2024 Mar 24.

  PMID: 38547656 RESULT

MeSH Terms

Conditions

Digestive System Diseases; Gallbladder Diseases; Pancreatic Diseases

Condition Hierarchy (Ancestors)

Biliary Tract Diseases

Study Officials

• ding yuan, doctor

  Second Affiliated Hospital, School of Medicine, Zhejiang University

  STUDY CHAIR

Central Study Contacts

ding yuan, doctor

CONTACT

18858101960; dingyuan@zju.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 4 Weeks
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 4, 2026
• First Posted: July 21, 2026
• Study Start (Estimated): August 15, 2026
• Primary Completion (Estimated): November 30, 2026
• Study Completion (Estimated): November 30, 2026
• Last Updated: July 21, 2026

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will not share