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

May 5, 2026 updated by: Junyan Li, The Third Affiliated Hospital of Southern Medical University

Prospective User Study and Multicenter Validation of Multimodal Medical Imaging Large Models in the Diagnosis of Common Systemic Diseases

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Background: Multimodal large models have shown significant potential in medical imaging. However, their performance and impact on clinical workflows across multiple centers require rigorous validation.

Objective: To assess the diagnostic performance of a multimodal large model and investigate whether AI assistance can improve the diagnostic accuracy and efficiency of radiologists with varying levels of experience.

Methodology: This research is designed as a multicenter, retrospective comparative reader study. A large-scale, diverse dataset of medical images (including CT and MRI) will be curated from the participating institutions. A group of licensed radiologists will perform diagnostic tasks in two separate sessions: a standalone session (without AI assistance) and an AI-assisted session, with a suitable washout period between sessions.

Data Analysis: The clinical "ground truth" will be established by expert consensus or histological results. The study will compare the Area Under the Receiver Operating Characteristic Curve (AUC), sensitivity, and specificity between the standalone and AI-assisted modes. Additionally, the reading time per case will be recorded to evaluate diagnostic efficiency.

Ethics: This study uses retrospective, anonymized data and does not alter the clinical management or treatment of patients.

The multimodal large model was developed and pre-trained using a massive dataset of approximately 1,000,000 medical imaging cases. This study focus on the multicenter clinical validation using an independent test cohort of 1,000 cases.

Study Type

Observational

Enrollment (Estimated)

1000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Guangdong
      • Guangzhou, Guangdong, China, 510630
        • Recruiting
        • The Third Affiliated Hospital of Southern Medical University
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

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.

Description

Inclusion Criteria:

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

Exclusion Criteria:

  • 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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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
Radiologists interpret the medical images independently without any assistance from the AI model to establish a baseline performance.
Other: AI-assisted Radiologist Interpretation
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Area Under the Receiver Operating Characteristic Curve (AUC)
Time Frame: Through study completion, approximately 12 months.
Evaluation of diagnostic accuracy using AUC to compare standalone radiologist performance versus AI-assisted performance.
Through study completion, approximately 12 months.

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Mean Reading and Reporting Time per Case
Time Frame: Through study completion, approximately 12 months.
Assessment of diagnostic efficiency by recording the time (in seconds) taken by radiologists to complete the diagnosis and generate reports, with and without AI assistance.
Through study completion, approximately 12 months.
Sensitivity and Specificity
Time Frame: Through study completion, approximately 12 months.
To calculate and compare the sensitivity and specificity of radiologists' diagnostic decisions in both standalone and AI-assisted sessions.
Through study completion, approximately 12 months.
Clinical Report Quality and Semantic Accuracy Score
Time Frame: Through study completion, approximately 12 months.
The quality of AI-generated reports will be evaluated by senior experts using a 5-point Likert scale, focusing on semantic accuracy, clinical relevance, and completeness of the descriptions. The scale ranges from 1 to 5, where 1 indicates "poor quality" and 5 indicates "excellent quality." Higher scores represent better report quality and higher semantic accuracy.
Through study completion, approximately 12 months.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

The Third Affiliated Hospital of Southern Medical University

Investigators

  • Principal Investigator: Yinghua Zhao, PhD, The Third Affiliated Hospital of Southern Medical University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2026

Primary Completion (Estimated)

December 1, 2026

Study Completion (Estimated)

December 1, 2026

Study Registration Dates

First Submitted

April 21, 2026

First Submitted That Met QC Criteria

April 21, 2026

First Posted (Actual)

April 28, 2026

Study Record Updates

Last Update Posted (Actual)

May 8, 2026

Last Update Submitted That Met QC Criteria

May 5, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • 2025-ER-141

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

To protect patient privacy and comply with institutional data security regulations.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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