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.

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.

Inclusion Criteria:

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

Exclusion Criteria:

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