AI-Assisted Pathologist Performance Improvement: A Multicenter, Prospective, Randomized Controlled Trial
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 pathology model, Control.
- Who it may be relevant to
- Registry conditions: Pathology Foundation Model. Basic parameters: from 18 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
Artificial Intelligence Model-Assisted Improvement of Pathologists' Performance in Clinical Diagnostic Tasks: A Multicenter, Prospective, Randomized Controlled Trial
Overview
The investigators plan to conduct a multicenter, prospective, randomized controlled trial to systematically evaluate the added value of pathology-based AI models in the gastric cancer diagnostic workflow. The study will focus on comparing AI-assisted platform interpretation with conventional independent slide reading in terms of diagnostic accuracy (e.g., AUC), reading efficiency (e.g., comparison of time to diagnosis), quality of diagnostic reports, diagnostic confidence (Likert scale), and pathologists' satisfaction with the AI models. The investigators will also assess superiority for less-experienced (junior) pathologists and noninferiority for more-experienced (senior) pathologists. Successful completion of this project will provide high-level prospective evidence to support the standardized deployment, quality control, and broader application of pathology AI in the gastric cancer care pathway.
Interventions
- Other AI pathology model
Doctors in this group are required to use the AI pathology 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 physicians' diagnoses.]
Secondary outcome measures (4)
- Diagnostic time per case [Time frame: Measured immediately after the physician's diagnosis.]
- Diagnostic report quality score [Time frame: Within 1 week after the initial diagnosis for each case.]
- Pathologists' diagnostic confidence [Time frame: At the time of diagnosis for each case.]
- Pathologists' satisfaction with the AI pathology model [Time frame: Assessed once at the end of the AI-assisted reading period for each pathologist.]
Eligibility criteria
Inclusion criteria
- Sex: ≥ 18 years of age;
- Patients undergoing gastric mucosal biopsy or gastric cancer surgical resection, with available digital pathology images and clinical information.
Exclusion criteria
1.Missing data or data of insufficient quality for analysis
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- Randomized
- Model
- Crossover
- Masking
- Single blind
- Primary purpose
- Other
Study locations
China · 2 centers
- Nanfang Hospital, Southern Medical University — Guangzhou
- The First Affiliated Hospital of Zhengzhou University — Zhengzhou
Identifiers
NCT: NCT07291362 · NFEC-2025-653