NCT07117266

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

A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 296 (actual)
• Sponsor: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Accepted

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.

Detailed description

There is a global shortage of radiologists, and the automatic report generation function of radiology AI systems is crucial for improving medical efficiency and meeting patient needs, especially those in areas with scarce medical resources. Multimodal large models have made it possible to develop automatic report generation systems for medical images. Although ChatGPT 4o has certain capabilities in medical image diagnosis, it has issues such as being closed-source and hallucination. The recently launched open-source multimodal large model Janus-Pro has advantages including high performance, "Any to any", low cost, and open-source; after training and fine-tuning, it has the potential for medical image diagnosis and report generation. However, there is currently a lack of research on the application of Janus Pro 1B in image diagnosis; existing models are mostly general-purpose, lacking in-depth optimization for specific fields and systematic multi-dimensional evaluation methods. This study aims to develop a large model specialized in medical images, Janus Pro 1B-CXR, using public databases, verify its application value in image diagnosis and radiology report generation, and construct an efficient and accurate automated medical image analysis and diagnostic assistance system.

Conditions

• X-Ray
• AI (Artificial Intelligence)
• Radiology

Interventions

Type	Name	Description
OTHER	radiologists reference AI reports	Radiologists generate reports with reference to AI reports

Timeline

• Start date: 2025-08-01
• Primary completion: 2025-08-12
• Completion: 2025-08-12
• First posted: 2025-08-12
• Last updated: 2025-09-12

Locations

3 sites across 1 country: China