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Multi-center Validation Study of a Large Language Model-based Intelligent Agent for Blood Cell Analysis

NCT07607184 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 20000

Last updated 2026-05-26

No results posted yet for this study

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: We need to recruit approximately 20,000 subjects, regardless of age or gender.

IV. Study Procedures: If you agree to participate in the study, you only need to allow us to use your test results after you have completed your routine blood test (CBC).

V. Risks and Benefits:

1. Risks: This study poses no risk to the subjects. We only use the result data of patients after they 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 your information will be kept strictly confidential and will only be used for this scientific research.

Conditions

  • Complete Blood Count Review

Interventions

OTHER

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).

Sponsors & Collaborators

  • Peking University First Hospital

    collaborator OTHER

  • Lishui hospital of Zhejiang University

    collaborator UNKNOWN

  • Huashan Hospital

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-05-14
Primary Completion
2027-08-31
Completion
2027-08-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07607184 on ClinicalTrials.gov