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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| AI-assisted group | Experimental | Doctors in this group are required to use the AI pathology diagnostic model to assist their diagnoses. The AI pathology model will provide a predicted result for each case. |
|
| Independent Diagnosis Group (Control Group) | Placebo Comparator | In this group, pathologists will independently diagnose each case based on their own clinical experience, and will record both their time to diagnosis and their diagnostic confidence. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI pathology model | Other | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Area under ROC curve (AUC) | Area under the curve | Assessments will be conducted within one week after the physicians' diagnoses. |
| Measure | Description | Time Frame |
|---|---|---|
| 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 | Measured immediately after the physician's diagnosis. |
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Inclusion Criteria:
Exclusion Criteria:
1.Missing data or data of insufficient quality for analysis
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| Name | Affiliation | Role |
|---|---|---|
| Li Liang | Nanfang Hospital, Southern Medical University | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Nanfang Hospital, Southern Medical University | Guangzhou | Guangdong | 510515 | China | ||
| The First Affiliated Hospital of Zhengzhou University |
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| Control | Other | Pathologists will independently diagnose each case based on their own clinical experience, and will record both their time to diagnosis and their diagnostic confidence. |
|
| 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. | 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. | 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. | Assessed once at the end of the AI-assisted reading period for each pathologist. |
| Zhengzhou |
| Henan |
| China |