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

24. mai 2026 oppdatert 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.

Studieoversikt

Status

Har ikke rekruttert ennå

Intervensjon / Behandling

Studietype

Observasjonsmessig

Registrering (Antatt)

20000

Deltakelseskriterier

Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.

Kvalifikasjonskriterier

Alder som er kvalifisert for studier

  • Barn
  • Voksen
  • Eldre voksen

Tar imot friske frivillige

Ja

Prøvetakingsmetode

Ikke-sannsynlighetsprøve

Studiepopulasjon

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.

Beskrivelse

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

Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.

Hvordan er studiet utformet?

Designdetaljer

Kohorter og intervensjoner

Gruppe / Kohort
Intervensjon / 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

Hva måler studien?

Primære resultatmål

Resultatmål
Tidsramme
Overall Report Turnaround Time
Tidsramme: one year
one year

Samarbeidspartnere og etterforskere

Det er her du vil finne personer og organisasjoner som er involvert i denne studien.

Studierekorddatoer

Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.

Studer hoveddatoer

Studiestart (Antatt)

14. mai 2026

Primær fullføring (Antatt)

31. august 2027

Studiet fullført (Antatt)

31. august 2027

Datoer for studieregistrering

Først innsendt

19. mai 2026

Først innsendt som oppfylte QC-kriteriene

19. mai 2026

Først lagt ut (Faktiske)

26. mai 2026

Oppdateringer av studieposter

Sist oppdatering lagt ut (Faktiske)

28. mai 2026

Siste oppdatering sendt inn som oppfylte QC-kriteriene

24. mai 2026

Sist bekreftet

1. mai 2026

Mer informasjon

Begreper knyttet til denne studien

Andre studie-ID-numre

  • KY2026-084

Denne informasjonen ble hentet direkte fra nettstedet clinicaltrials.gov uten noen endringer. Hvis du har noen forespørsler om å endre, fjerne eller oppdatere studiedetaljene dine, vennligst kontakt register@clinicaltrials.gov. Så snart en endring er implementert på clinicaltrials.gov, vil denne også bli oppdatert automatisk på nettstedet vårt. .

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