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Multi-center Validation Study of a Large Language Model-based Intelligent Agent for Blood Cell Analysis
Sponsor: Huashan Hospital
Summary
I. Study Background: Currently, in most medical institutions, the review of blood cell analysis still heavily relies on manual verification by laboratory staff. This process requires a comprehensive analysis of instrument parameters, alarm flags, historical comparison results, and, when necessary, microscopic examination. However, with the increasing volume of test samples and the high concentration of review tasks during peak hours, the traditional manual review model increasingly shows problems such as prolonged turnaround time (TAT), uneven workload distribution, and decreased consistency in reviews. In recent years, intelligent review systems based on Large Language Models (LLM) have shown potential in analyzing abnormal results and stratifying sample risks by integrating preset rules, clinical diagnostic information, and multi-dimensional laboratory data, which is expected to optimize the review workflow. II. Study Objective: To evaluate the difference in overall sample review turnaround time between the experimental process and the control process during the formal study phase, and to test its superiority. III. Subjects: The investigators need to recruit approximately 20,000 subjects, regardless of age or gender. IV. Study Procedures: If participants agree to participate in the study, participants only need to allow us to use participants test results after participants have completed your routine blood test (CBC). V. Risks and Benefits: 1. Risks: This study poses no risk to the subjects. The investigators only use the result data of patients after participants have had their routine blood test; there is no need for patients to undergo additional blood draws. 2. Benefits: It will shorten the turnaround time for routine blood test results and share the workload of doctors in reviewing these results. VI. Privacy: All of participants information will be kept strictly confidential and will only be used for this scientific research.
Key Details
Gender
All
Age Range
Any - Any
Study Type
OBSERVATIONAL
Enrollment
20000
Start Date
2026-05-14
Completion Date
2027-08-31
Last Updated
2026-05-28
Healthy Volunteers
Yes
Conditions
Interventions
LLM-Assisted Review Group
This study introduces an intelligent auxiliary review system based on a medical Large Language Model (LLM), aimed at optimizing the traditional CBC report review process. The core functions and intervention mechanisms are as follows: Multi-source Data Integration: The system integrates seamlessly with the Laboratory Information System (LIS) to automatically retrieve patient demographics (age, sex), current CBC indices, historical results, and clinical diagnoses. Deep Analysis and Anomaly Detection: Unlike traditional rule-based auto-verification, this system leverages the reasoning capability of LLMs to perform multidimensional clinical logic checks. It identifies out-of-range values and interprets their clinical significance by combining them with patient history (e.g., distinguishing physiological fluctuations from pathological changes).