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

15 lipca 2026 zaktualizowane przez: 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.

Przegląd badań

Status

Jeszcze nie rekrutacja

Warunki

Interwencja / Leczenie

Szczegółowy opis

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.

Typ studiów

Obserwacyjny

Zapisy (Szacowany)

400

Kontakty i lokalizacje

Ta sekcja zawiera dane kontaktowe osób prowadzących badanie oraz informacje o tym, gdzie badanie jest przeprowadzane.

Kontakt w sprawie studiów

Kryteria uczestnictwa

Badacze szukają osób, które pasują do określonego opisu, zwanego kryteriami kwalifikacyjnymi. Niektóre przykłady tych kryteriów to ogólny stan zdrowia danej osoby lub wcześniejsze leczenie.

Kryteria kwalifikacji

Wiek uprawniający do nauki

  • Dorosły
  • Starszy dorosły

Akceptuje zdrowych ochotników

Tak

Metoda próbkowania

Próbka prawdopodobieństwa

Badana populacja

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.

Opis

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).

Plan studiów

Ta sekcja zawiera szczegółowe informacje na temat planu badania, w tym sposób zaprojektowania badania i jego pomiary.

Jak projektuje się badanie?

Szczegóły projektu

Kohorty i interwencje

Grupa / Kohorta
Interwencja / Leczenie
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.
Inny: 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.
Inny: 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.

Co mierzy badanie?

Podstawowe miary wyniku

Miara wyniku
Opis środka
Ramy czasowe
Concordance Rate Between AI-Generated Medical Records and Gold Standard
Ramy czasowe: 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
Ramy czasowe: 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
Ramy czasowe: 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

Miary wyników drugorzędnych

Miara wyniku
Opis środka
Ramy czasowe
Average physician consultation duration
Ramy czasowe: 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
Ramy czasowe: 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
Ramy czasowe: 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
Ramy czasowe: 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
Ramy czasowe: 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
Ramy czasowe: 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

Współpracownicy i badacze

Tutaj znajdziesz osoby i organizacje zaangażowane w to badanie.

Sponsor

Second Affiliated Hospital, School of Medicine, Zhejiang University

Śledczy

  • Krzesło do nauki: ding yuan, doctor, Second Affiliated Hospital, School of Medicine, Zhejiang University

Publikacje i pomocne linki

Osoba odpowiedzialna za wprowadzenie informacji o badaniu dobrowolnie udostępnia te publikacje. Mogą one dotyczyć wszystkiego, co jest związane z badaniem.

Publikacje ogólne

Daty zapisu na studia

Daty te śledzą postęp w przesyłaniu rekordów badań i podsumowań wyników do ClinicalTrials.gov. Zapisy badań i zgłoszone wyniki są przeglądane przez National Library of Medicine (NLM), aby upewnić się, że spełniają określone standardy kontroli jakości, zanim zostaną opublikowane na publicznej stronie internetowej.

Główne daty studiów

Rozpoczęcie studiów (Szacowany)

15 sierpnia 2026

Zakończenie podstawowe (Szacowany)

30 listopada 2026

Ukończenie studiów (Szacowany)

30 listopada 2026

Daty rejestracji na studia

Pierwszy przesłany

4 marca 2026

Pierwszy przesłany, który spełnia kryteria kontroli jakości

15 lipca 2026

Pierwszy wysłany (Rzeczywisty)

21 lipca 2026

Aktualizacje rekordów badań

Ostatnia wysłana aktualizacja (Rzeczywisty)

21 lipca 2026

Ostatnia przesłana aktualizacja, która spełniała kryteria kontroli jakości

15 lipca 2026

Ostatnia weryfikacja

1 grudnia 2025

Więcej informacji

Terminy związane z tym badaniem

Słowa kluczowe

Dodatkowe istotne warunki MeSH

Inne numery identyfikacyjne badania

  • 2025-1371

Plan dla danych uczestnika indywidualnego (IPD)

Planujesz udostępniać dane poszczególnych uczestników (IPD)?

NIE

Opis planu IPD

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

Informacje o lekach i urządzeniach, dokumenty badawcze

Bada produkt leczniczy regulowany przez amerykańską FDA

Nie

Bada produkt urządzenia regulowany przez amerykańską FDA

Nie

Te informacje zostały pobrane bezpośrednio ze strony internetowej clinicaltrials.gov bez żadnych zmian. Jeśli chcesz zmienić, usunąć lub zaktualizować dane swojego badania, skontaktuj się z register@clinicaltrials.gov. Gdy tylko zmiana zostanie wprowadzona na stronie clinicaltrials.gov, zostanie ona automatycznie zaktualizowana również na naszej stronie internetowej .

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Badania kliniczne na AI Integration type 1

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