Clinical Application of Automated Interpretation System for Chest X-Ray Images Based on Multimodal Large Models

NCT07117266 | Completed DMC

• 1 other identifier: Janus Pro 1B-CXR
• Study Type: interventional
• Target: 296
• Locations: 1 country
• Sites: 3

Brief Summary

There's a global shortage of radiologists. Radiology AI's automatic reporting is key for boosting efficiency and meeting patient needs, especially in resource-poor areas. Multimodal large models enable medical image auto-reporting systems. ChatGPT 4o can diagnose medical images but has issues like being closed-source and "hallucinations." The new open-source Janus Pro 1B-with strong performance, "any-to-any" capability, low cost, and open access-shows potential for medical imaging tasks with training. But little research explores its use here; most models are general, lacking field-specific optimization and systematic evaluation. This study will develop Janus Pro 1B-CXR (a medical image-specific model) via public data, test its value in diagnosis and reporting, and build an efficient automated system.

Conditions

X-Ray; AI (Artificial Intelligence); Radiology

Keywords

radiology; x-ray; AI

Outcome Measures

Primary Outcomes (4)

• Report quality scores in the prospective study

  In this prospective study, the quality of reports generated by junior radiologists was assessed using a 5-point Likert scale titled "Radiology Report Quality Assessment Scale", where the minimum value is 1 and the maximum value is 5, with higher scores indicating better report quality. These scores were compared between the AI-assisted group (junior radiologists using AI tools for report generation) and the standard care group (junior radiologists generating reports without AI assistance).

  1 week

• Agreement evaluation in the prospective study

  In this prospective study, the agreement between reports generated by junior radiologists and standard reports was assessed using the RADPEER scoring system-a peer review program established by the American College of Radiology (ACR) designed to evaluate the interpretation accuracy of radiologists-where the degree of concordance is measured by grading discrepancies and agreements according to specific criteria that also account for the clinical significance of differences. The RADPEER system uses a 5-category scale with a minimum value of 1 and a maximum value of 5, where higher scores indicate greater agreement between the generated reports and standard reports.

  1 week

• Pairwise preference tests in the prospective study

  In this prospective study, the preference between reports generated by junior radiologists in the AI-assisted group versus the standard care group was evaluated using the "Expert Pairwise Preference Assessment Tool", a structured measurement tool designed to quantify expert consensus on report superiority. The assessment was conducted by a panel of 5 independent radiology experts, who reviewed paired reports (one from the AI-assisted group and one from the standard-care group for the same clinical case) and individually indicated their preference for which report was more clinically valuable, accurate, or comprehensive. The unit of measure for this outcome is the "Percentage of paired cases with majority expert preference", defined as cases where ≥3 out of 5 experts expressed a clear preference for either the AI-assisted or standard care report.

  1 week

• Reading Time in the prospective study

  The time from when radiologists began examining chest radiographs to the completion of final reports, comparing efficiency between the AI-assisted and Standard-care groups.

  1 week

Secondary Outcomes (3)

• Report Quality Score in the retrospective study

  1 week

• Agreement Evaluation in the retrospective study

  1 week

• Pairwise preference tests in the retrospective study

  1 week

Study Arms (2)

AI-assisted group

EXPERIMENTAL

Radiologists generate reports with reference to AI reports

Other: radiologists reference AI reports

Standard care group

NO INTERVENTION

Radiologists generate reports independently without referencing AI reports, following standard clinical procedures.

Interventions

radiologists reference AI reports OTHER

Radiologists generate reports with reference to AI reports

AI-assisted group

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• clinically suspected thoracic diseases (such as pneumonia, tuberculosis, or lung cancer) requiring CXR-assisted diagnosis;
• patients providing written informed consent for research data use;
• complete clinical records (including chief complaints, medical history, and laboratory test results);
• patients with no historical chest X-ray images and no need for comparison with previous chest X-ray images;
• Patients who underwent only posteroanterior (PA) chest X-rays without lateral chest X-rays.

You may not qualify if:

• substandard CXR image quality (including severe motion artifacts, over-/underexposure, or missing anatomical structures);
• pregnant or lactating women.

Sponsors & Collaborators

• Union Hospital, Tongji Medical College, Huazhong University of Science and Technology: lead
• The First Affiliated Hospital of Zhengzhou University: collaborator
• The First Affiliated Hospital of Henan University of Science and Technology: collaborator

Study Sites (3)

The First Affiliated Hospital of Henan University of science and technology

Luoyang, 471003, China

Location

Union Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology

Wuhan, 430023, China

Location

The First Affiliated Hospital of Zhengzhou University

Zhengzhou, 450002, China

Location

Related Publications (3)

• Choi Y. Leveraging GPT-4 as a Proofreader: Addressing the Growing Workload of Radiologists. Radiology. 2025 Jan;314(1):e243859. doi: 10.1148/radiol.243859. No abstract available.

  PMID: 39873608 BACKGROUND

• Rimmer A. Radiologist shortage leaves patient care at risk, warns royal college. BMJ. 2017 Oct 11;359:j4683. doi: 10.1136/bmj.j4683. No abstract available.

  PMID: 29021184 BACKGROUND

• Afshari Mirak S, Tirumani SH, Ramaiya N, Mohamed I. The Growing Nationwide Radiologist Shortage: Current Opportunities and Ongoing Challenges for International Medical Graduate Radiologists. Radiology. 2025 Mar;314(3):e232625. doi: 10.1148/radiol.232625.

  PMID: 40035678 BACKGROUND

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: PARTICIPANT, OUTCOMES ASSESSOR
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Investigator

Study Record Dates

• First Submitted: July 10, 2025
• First Posted: August 12, 2025
• Study Start: August 1, 2025
• Primary Completion: August 12, 2025
• Study Completion: August 12, 2025
• Last Updated: September 12, 2025

Record last verified: 2025-08

Locations

CN (3)