EviAgent: Evidence-Driven Agent for Radiology Report Generation
arXiv:2603.13956v1 Announce Type: new Abstract: Automated radiology report generation holds immense potential to alleviate the heavy workload of radiologists. Despite the formidable vision-language capabilities of recent Multimodal Large Language Models (MLLMs), their clinical deployment is severely constrained by inherent limitations: their "black-box" decision-making renders the generated reports untraceable due to the lack of explicit visual evidence to support the diagnosis, and they struggle to access external domain knowledge. To address these challenges, we propose the Evidence-driven Radiology Report Generation Agent (EviAgent). Unlike opaque end-to-end paradigms, EviAgent coordinates a transparent reasoning trajectory by breaking down the complex generation process into granular operational units. We integrate multi-dimensional visual experts and retrieval mechanisms as external support modules, endowing the system with explicit visual evidence and high-quality clinical prior
arXiv:2603.13956v1 Announce Type: new Abstract: Automated radiology report generation holds immense potential to alleviate the heavy workload of radiologists. Despite the formidable vision-language capabilities of recent Multimodal Large Language Models (MLLMs), their clinical deployment is severely constrained by inherent limitations: their "black-box" decision-making renders the generated reports untraceable due to the lack of explicit visual evidence to support the diagnosis, and they struggle to access external domain knowledge. To address these challenges, we propose the Evidence-driven Radiology Report Generation Agent (EviAgent). Unlike opaque end-to-end paradigms, EviAgent coordinates a transparent reasoning trajectory by breaking down the complex generation process into granular operational units. We integrate multi-dimensional visual experts and retrieval mechanisms as external support modules, endowing the system with explicit visual evidence and high-quality clinical priors. Extensive experiments on MIMIC-CXR, CheXpert Plus, and IU-Xray datasets demonstrate that EviAgent outperforms both large-scale generalist models and specialized medical models, providing a robust and trustworthy solution for automated radiology report generation.
Executive Summary
The article presents EviAgent, a novel evidence-driven agent for automated radiology report generation. By breaking down the complex generation process into granular operational units, EviAgent coordinates a transparent reasoning trajectory and integrates multi-dimensional visual experts and retrieval mechanisms as external support modules. This approach enables the system to provide explicit visual evidence and high-quality clinical priors, addressing the limitations of recent Multimodal Large Language Models (MLLMs). The proposed system outperforms both generalist models and specialized medical models in extensive experiments on various radiology datasets, demonstrating its robustness and trustworthiness. This advancement has the potential to alleviate the heavy workload of radiologists and improve the accuracy of radiology reports.
Key Points
- ▸ EviAgent is an evidence-driven agent for radiology report generation that addresses the limitations of MLLMs.
- ▸ The system coordinates a transparent reasoning trajectory by breaking down the generation process into granular operational units.
- ▸ EviAgent integrates multi-dimensional visual experts and retrieval mechanisms as external support modules.
Merits
Strength
EviAgent's transparent reasoning trajectory and integration of explicit visual evidence and high-quality clinical priors provide a robust and trustworthy solution for automated radiology report generation.
Improved Accuracy
EviAgent outperforms both generalist models and specialized medical models in extensive experiments on various radiology datasets, demonstrating its potential to improve the accuracy of radiology reports.
Demerits
Limitation
The proposed system may require significant computational resources and expertise to implement and maintain, which could limit its adoption in resource-constrained settings.
Scalability
The effectiveness of EviAgent in large-scale settings and its ability to adapt to diverse radiology datasets may require further investigation.
Expert Commentary
The proposed EviAgent system represents a significant advancement in the field of automated radiology report generation. By addressing the limitations of MLLMs and providing a transparent reasoning trajectory, EviAgent has the potential to improve the accuracy and trustworthiness of radiology reports. However, its adoption in clinical settings will require careful consideration of the potential challenges, including computational resource requirements and scalability. Furthermore, the implications of EviAgent on the development of automated medical reporting systems and clinical decision support systems warrant further investigation.
Recommendations
- ✓ Future research should focus on addressing the computational resource requirements and scalability of EviAgent, as well as its adaptability to diverse radiology datasets.
- ✓ The development of EviAgent should be accompanied by comprehensive clinical validation and the establishment of robust regulatory frameworks governing its use in clinical settings.