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

NCT07117266 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 296

Last updated 2025-09-12

No results posted yet for this study

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

Interventions

OTHER

radiologists reference AI reports

Radiologists generate reports with reference to AI reports

Sponsors & Collaborators

  • The First Affiliated Hospital of Zhengzhou University

    collaborator OTHER

  • The First Affiliated Hospital of Henan University of Science and Technology

    collaborator OTHER

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

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
DOUBLE
Model
PARALLEL

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-08-01
Primary Completion
2025-08-12
Completion
2025-08-12

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07117266 on ClinicalTrials.gov