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Not yet recruiting NCT07408167

AI Models in Clinical Pathology Diagnosis: A Multicenter RCT

No phase Interventional Pathology Foundation Model

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: AI model, Control.
Who it may be relevant to
Registry conditions: Pathology Foundation Model. Basic parameters: 18 years — 100 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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Official title

Performance of AI Models in the Clinical Pathology Diagnostic Workflow: A Multicenter, Prospective, Randomized Controlled Trial

Overview

The investigators plan to conduct a multicenter, prospective, randomized controlled trial to systematically evaluate the incremental value of pathology-based artificial intelligence (AI) models in a pan-disease diagnostic workflow. The study will primarily compare interpretation using an AI-assisted platform with conventional independent slide reading in terms of diagnostic accuracy (e.g., AUC), reading efficiency (e.g., diagnostic time), diagnostic report quality, diagnostic confidence (Likert scale), and pathologists' satisfaction with the AI model. Investigators will also assess superiority among less experienced (junior) pathologists and non-inferiority among more experienced (senior) pathologists. Successful completion of this project will provide high-level prospective evidence to support standardized deployment, quality control, and broader implementation of pathology AI in clinical practice. This trial may also evaluate the potential benefits and risks of using AI tools in medical research.

Detailed description

In this study, investigators plan to enroll 60 pathologists with varying levels of experience and 2,000 patients requiring pathological diagnosis, with whole-slide images (WSIs) collected.

Interventions

  • Other AI model
    Doctors in this group are required to use the AI model to assist their diagnoses. The AI pathology model will provide a predicted result for each case.
  • Other Control
    Pathologists will independently diagnose each case based on their own clinical experience, and will record both their time to diagnosis and their diagnostic confidence.

Primary outcome measures

  • Area under ROC curve (AUC) [Time frame: Assessments will be conducted within one week after the pathologists' diagnoses]
Secondary outcome measures (2)
  • Diagnostic time per case [Time frame: Measured immediately after the pathologists' diagnosis]
  • Pathologists' diagnostic confidence [Time frame: At the time of diagnosis for each case.]

Eligibility criteria

Pathologists:

Inclusion criteria

  • Voluntarily provide written informed consent.
  • Age ≥ 20 years.
  • Have completed at least 1 year of training in pathological diagnosis.

Exclusion criteria

  • Individuals with reading difficulties or a reading disorder.
  • Unwilling to participate in this study.

Patients:

Inclusion criteria

  • Voluntarily provide written informed consent.
  • Age ≥ 18 years.
  • Have available digital pathology images and relevant clinical information.

Exclusion criteria

  • Missing data or data quality not meeting the requirements for analysis.
  • Deemed unsuitable for participation by the investigator.
  • Unwilling to participate in this study.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Allocation
Randomized
Model
Crossover
Masking
Single blind
Primary purpose
Other

Study locations

China · 3 centers
  • The First Hospital Affiliated to AMU SOUTHWEST HOSPITAL — Chongqing
  • Nanfang Hospital, Southern Medical University — Guangzhou
  • The First Affiliated Hospital of Zhengzhou University — Zhengzhou

Identifiers

NCT: NCT07408167 · NFEC-2026-059

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗