Development and Prospective Validation of an AI-Based Diagnostic Model for Hepato-Pancreato-Biliary Diseases
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 400
- 主要终点
- Concordance Rate Between AI-Generated Medical Records and Gold Standard
研究概览
简要总结
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.
详细描述
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:
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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.
排除标准
- •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).
结局指标
主要结局
Concordance Rate Between AI-Generated Medical Records and Gold Standard
时间窗: 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).
Diagnostic Accuracy of Physician Documentation With AI Summary Reference
时间窗: 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).
Proportion of physician medical records meeting predefined quality criteria without AI support
时间窗: 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.
次要结局
- 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)
- System Usability Scale (SUS) scores for the AI interface(From enrollment through completion of the index outpatient visit and chart assessment, assessed up to 7 days)