Prospective User Study and Multicenter Validation of Multimodal Medical Imaging Large Models

NCT07555002 | Recruiting

• 1 other identifier: 2025-ER-141
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief Summary

This study aims to evaluate the diagnostic performance and clinical utility of a multimodal medical imaging large model in identifying common systemic diseases. Through a retrospective reader study involving multiple centers, the research will compare the diagnostic accuracy, sensitivity, and specificity of radiologists with and without AI assistance. The goal is to validate the model's robustness and its impact on the diagnostic efficiency of clinicians across diverse healthcare settings.

Conditions

Diagnostic Imaging; Common Systemic Diseases; Artificial Intelligence (AI)

Keywords

Multimodal Large Model; Deep Learning; Radiology; Multicenter Study; Diagnostic Performance

Outcome Measures

Primary Outcomes (1)

• Area Under the Receiver Operating Characteristic Curve (AUC)

  Evaluation of diagnostic accuracy using AUC to compare standalone radiologist performance versus AI-assisted performance.

  Through study completion, approximately 12 months.

Secondary Outcomes (3)

• Mean Reading and Reporting Time per Case

  Through study completion, approximately 12 months.

• Clinical Report Quality and Semantic Accuracy Score

  Through study completion, approximately 12 months.

• Sensitivity and Specificity

  Through study completion, approximately 12 months.

Study Arms (1)

Validation Cohort

A retrospective dataset of medical imaging cases (including CT and MRI) collected from multiple centers, representing common systemic diseases, used to evaluate the diagnostic performance of the multimodal large model.

Other: Standalone Radiologist Interpretation; Other: AI-assisted Radiologist Interpretation

Interventions

Standalone Radiologist Interpretation OTHER

Radiologists interpret the medical images independently without any assistance from the AI model to establish a baseline performance.

Validation Cohort

AI-assisted Radiologist Interpretation OTHER

Radiologists interpret the same set of medical images with the assistance of the multimodal medical imaging large model to evaluate the improvement in diagnostic performance.

Validation Cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients from multiple medical centers in China who underwent systemic medical imaging for various clinical indications, representing a broad range of common systemic diseases.

You may qualify if:

• Patients who underwent systemic medical imaging examinations (e.g., CT or MRI) at participating centers for common systemic diseases.
• Imaging data must have a confirmed clinical reference standard, expert consensus, or pathological diagnosis.
• Availability of complete DICOM format images with standard acquisition protocols.

You may not qualify if:

• Poor image quality (e.g., severe motion or metal artifacts) that precludes definitive diagnosis.
• Cases with incomplete clinical or pathological reference standards. Corrupted image files or duplicate cases.

Sponsors & Collaborators

• The Third Affiliated Hospital of Southern Medical University: lead

Study Sites (1)

The Third Affiliated Hospital of Southern Medical University

Guangzhou, Guangdong, 510630, China

RECRUITING

Study Officials

• Yinghua Zhao, PhD

  The Third Affiliated Hospital of Southern Medical University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Tao Li, MD

CONTACT

+86-15527360835; lt12420131@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER GOV
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Scientific Research Administrator

Study Record Dates

• First Submitted: April 21, 2026
• First Posted: April 28, 2026
• Study Start: January 1, 2026
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 1, 2026
• Last Updated: April 28, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)