Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.18530 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916756474298368 |
|---|---|
| author | Wang, Pengyu Ye, Shuchang Naseem, Usman Kim, Jinman |
| author_facet | Wang, Pengyu Ye, Shuchang Naseem, Usman Kim, Jinman |
| contents | Medical Large Vision-Language Models (Med-LVLMs) have been widely adopted for medical report generation. Despite Med-LVLMs producing state-of-the-art performance, they exhibit a bias toward predicting all findings as normal, leading to reports that overlook critical abnormalities. Furthermore, these models often fail to provide comprehensive descriptions of radiologically relevant regions necessary for accurate diagnosis. To address these challenges, we proposeMedical Report Generation Agents (MRGAgents), a novel multi-agent framework that fine-tunes specialized agents for different disease categories. By curating subsets of the IU X-ray and MIMIC-CXR datasets to train disease-specific agents, MRGAgents generates reports that more effectively balance normal and abnormal findings while ensuring a comprehensive description of clinically relevant regions. Our experiments demonstrate that MRGAgents outperformed the state-of-the-art, improving both report comprehensiveness and diagnostic utility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18530 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MRGAgents: A Multi-Agent Framework for Improved Medical Report Generation with Med-LVLMs Wang, Pengyu Ye, Shuchang Naseem, Usman Kim, Jinman Multiagent Systems Artificial Intelligence Medical Large Vision-Language Models (Med-LVLMs) have been widely adopted for medical report generation. Despite Med-LVLMs producing state-of-the-art performance, they exhibit a bias toward predicting all findings as normal, leading to reports that overlook critical abnormalities. Furthermore, these models often fail to provide comprehensive descriptions of radiologically relevant regions necessary for accurate diagnosis. To address these challenges, we proposeMedical Report Generation Agents (MRGAgents), a novel multi-agent framework that fine-tunes specialized agents for different disease categories. By curating subsets of the IU X-ray and MIMIC-CXR datasets to train disease-specific agents, MRGAgents generates reports that more effectively balance normal and abnormal findings while ensuring a comprehensive description of clinically relevant regions. Our experiments demonstrate that MRGAgents outperformed the state-of-the-art, improving both report comprehensiveness and diagnostic utility. |
| title | MRGAgents: A Multi-Agent Framework for Improved Medical Report Generation with Med-LVLMs |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2505.18530 |