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

24 maj 2026 uppdaterad av: Ming Guan, Huashan Hospital

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.

Studieöversikt

Status

Har inte rekryterat ännu

Intervention / Behandling

Studietyp

Observationell

Inskrivning (Beräknad)

20000

Deltagandekriterier

Forskare letar efter personer som passar en viss beskrivning, så kallade behörighetskriterier. Några exempel på dessa kriterier är en persons allmänna hälsotillstånd eller tidigare behandlingar.

Urvalskriterier

Åldrar som är berättigade till studier

  • Barn
  • Vuxen
  • Äldre vuxen

Tar emot friska volontärer

Ja

Testmetod

Icke-sannolikhetsprov

Studera befolkning

The study subjects were consecutive individuals undergoing routine blood tests at each center, with no restrictions on gender or age. After inclusion, samples entered the corresponding review process based on the study week, with preliminary and secondary reviews conducted by predefined workflows and qualified personnel, respectively.

Beskrivning

Inclusion Criteria:

  • Subjects who underwent routine blood tests in the outpatient, emergency, or inpatient departments of the participating centers during the study period.

Corresponding samples must have complete instrument results, review trails, and report timestamp records.

Approved for inclusion by the Ethics Committee.

Exclusion Criteria:

  • Samples collected during periods of instrument malfunction or interface transmission anomalies.

Missing key research data, particularly samples where the final review conclusion or key timestamps cannot be confirmed.

Subjects or their legal representatives explicitly refuse to participate in the study.

Studieplan

Det här avsnittet ger detaljer om studieplanen, inklusive hur studien är utformad och vad studien mäter.

Hur är studien utformad?

Designdetaljer

Kohorter och interventioner

Grupp / Kohort
Intervention / Behandling
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).

Standard Manual Review Group

Vad mäter studien?

Primära resultatmått

Resultatmått
Tidsram
Overall Report Turnaround Time
Tidsram: one year
one year

Samarbetspartners och utredare

Det är här du hittar personer och organisationer som är involverade i denna studie.

Studieavstämningsdatum

Dessa datum spårar framstegen för inlämningar av studieposter och sammanfattande resultat till ClinicalTrials.gov. Studieposter och rapporterade resultat granskas av National Library of Medicine (NLM) för att säkerställa att de uppfyller specifika kvalitetskontrollstandarder innan de publiceras på den offentliga webbplatsen.

Studera stora datum

Studiestart (Beräknad)

14 maj 2026

Primärt slutförande (Beräknad)

31 augusti 2027

Avslutad studie (Beräknad)

31 augusti 2027

Studieregistreringsdatum

Först inskickad

19 maj 2026

Först inskickad som uppfyllde QC-kriterierna

19 maj 2026

Första postat (Faktisk)

26 maj 2026

Uppdateringar av studier

Senaste uppdatering publicerad (Faktisk)

28 maj 2026

Senaste inskickade uppdateringen som uppfyllde QC-kriterierna

24 maj 2026

Senast verifierad

1 maj 2026

Mer information

Termer relaterade till denna studie

Andra studie-ID-nummer

  • KY2026-084

Denna information hämtades direkt från webbplatsen clinicaltrials.gov utan några ändringar. Om du har några önskemål om att ändra, ta bort eller uppdatera dina studieuppgifter, vänligen kontakta register@clinicaltrials.gov. Så snart en ändring har implementerats på clinicaltrials.gov, kommer denna att uppdateras automatiskt även på vår webbplats .

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