AI-Assisted Pathologist Performance Improvement: A Multicenter, Prospective, Randomized Controlled Trial

NCT07291362 | Enrolling By Invitation

• 1 other identifier: NFEC-2025-653
• Study Type: interventional
• Target: 1,000
• Locations: 1 country
• Sites: 2

Brief Summary

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.

Conditions

Pathology Foundation Model

Keywords

Randomized Controlled Trial

Outcome Measures

Primary Outcomes (1)

• Area under ROC curve (AUC)

  Area under the curve

  Assessments will be conducted within one week after the physicians' diagnoses.

Secondary Outcomes (4)

• Diagnostic time per case

  Measured immediately after the physician's diagnosis.

• Diagnostic report quality score

  Within 1 week after the initial diagnosis for each case.

• Pathologists' diagnostic confidence

  At the time of diagnosis for each case.

• Pathologists' satisfaction with the AI pathology model

  Assessed once at the end of the AI-assisted reading period for each pathologist.

Study Arms (2)

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.

Other: AI pathology model

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.

Other: Control

Interventions

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.

AI-assisted group

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.

Independent Diagnosis Group (Control Group)

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Sex: ≥ 18 years of age;
• Patients undergoing gastric mucosal biopsy or gastric cancer surgical resection, with available digital pathology images and clinical information.

You may not qualify if:

• Missing data or data of insufficient quality for analysis

Sponsors & Collaborators

• Nanfang Hospital, Southern Medical University: lead

Study Sites (2)

Nanfang Hospital, Southern Medical University

Guangzhou, Guangdong, 510515, China

Location

The First Affiliated Hospital of Zhengzhou University

Zhengzhou, Henan, China

Location

Study Officials

• Li Liang

  Nanfang Hospital, Southern Medical University

  STUDY DIRECTOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Purpose: OTHER
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 5, 2025
• First Posted: December 18, 2025
• Study Start: November 1, 2025
• Primary Completion (Estimated): June 1, 2027
• Study Completion (Estimated): November 1, 2027
• Last Updated: February 10, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (2)