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.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
OTHER
Masking
SINGLE
Enrollment
1,000
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.
Pathologists will independently diagnose each case based on their own clinical experience, and will record both their time to diagnosis and their diagnostic confidence.
Nanfang Hospital, Southern Medical University
Guangzhou, Guangdong, China
The First Affiliated Hospital of Zhengzhou University
Zhengzhou, Henan, China
Area under ROC curve (AUC)
Area under the curve
Time frame: Assessments will be conducted within one week after the physicians' diagnoses.
Diagnostic time per case
Time required for the pathologist to complete the diagnosis of each case in the AI-assisted diagnosis group compared with the independent diagnosis group. Diagnostic time is defined as the duration (in minutes/seconds) from initiating case review to finalizing and submitting the diagnostic report in the study system
Time frame: Measured immediately after the physician's diagnosis.
Diagnostic report quality score
Quality score of pathology diagnostic reports in the AI-assisted diagnosis group compared with the independent diagnosis group. Report quality will be evaluated by an independent panel of expert pathologists using a predefined scoring rubric (e.g., 0-100 scale), considering diagnostic accuracy, completeness, clarity, and structure of the report. Higher scores indicate better report quality.
Time frame: Within 1 week after the initial diagnosis for each case.
Pathologists' diagnostic confidence
Self-reported diagnostic confidence of pathologists for each case in the AI-assisted diagnosis group compared with the independent diagnosis group. Diagnostic confidence will be rated by the reporting pathologist on a \[5\]-point Likert scale (e.g., 1 = very uncertain to 5 = very confident) immediately after completing the diagnosis. Higher scores indicate greater diagnostic confidence.
Time frame: At the time of diagnosis for each case.
Pathologists' satisfaction with the AI pathology model
Overall satisfaction of pathologists with the AI pathology diagnostic model in terms of usability and perceived effectiveness. Satisfaction will be assessed using a structured questionnaire comprising Likert-scale items that evaluate ease of use, integration into workflow, clarity of AI outputs, perceived impact on diagnostic efficiency, and perceived impact on diagnostic accuracy and confidence. Higher scores indicate higher satisfaction, better usability, and greater perceived effectiveness.
Time frame: Assessed once at the end of the AI-assisted reading period for each pathologist.
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