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Enrolling by Invitation

NCT Number: NCT07291362

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

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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Key information

Conditions

Pathology Foundation Model

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China

Loading trial locations.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

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.

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.

Primary outcomes

  1. Area under ROC curve (AUC)

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

    Area under the curve

Secondary outcomes

  1. Diagnostic time per case

    Time frame: Measured immediately after the physician's diagnosis.

    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

  2. Diagnostic report quality score

    Time frame: Within 1 week after the initial diagnosis for each case.

    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.

  3. Pathologists' diagnostic confidence

    Time frame: At the time of diagnosis for each case.

    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.

  4. Pathologists' satisfaction with the AI pathology model

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

    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.

Sponsors and collaborators

Lead sponsor

Nanfang Hospital, Southern Medical University

Other

Registry information

Official study title

Artificial Intelligence Model-Assisted Improvement of Pathologists' Performance in Clinical Diagnostic Tasks: A Multicenter, Prospective, Randomized Controlled Trial

Important dates

Study start
2025
Primary completion
2027
Study completion
2027
First posted
Dec 18, 2025
Registry last updated
Feb 10, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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