EviAgent: Evidence-Driven Agent for Radiology Report Generation

• URL: http://arxiv.org/abs/2603.13956v1
• Date: Sat, 14 Mar 2026 14:07:55 GMT
• Title: EviAgent: Evidence-Driven Agent for Radiology Report Generation
• Abstract summary: We propose the Evidence-driven Radiology Report Generation Agent (EviAgent)<n>Unlike opaque end-to-end paradigms, EviAgent coordinates a transparent reasoning trajectory by breaking down the complex generation process into granular operational units.<n>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.
• Score: 2.624902795082451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Concept-Grounded Reasoning with Prompt-Driven Localization for Interpretable Structured Report Generation [66.62662330558906]
  CORAL is a multimodal framework that integrates spatial grounding and concept-level supervision into a unified reasoning process.<n>CORAL employs a prompt-driven medical segmentation model to localize lesions and predicts multi-class clinical attributes through a Concept Bottleneck module.<n>Experiments on BUS-CoT and IU X-ray datasets demonstrate consistent improvements in diagnostic accuracy, concept consistency, and report quality over strong general-purpose and medical MLLMs.
  arXiv  Detail & Related papers  (2026-09-14T10:21:23Z)
• MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation [20.858060411945438]
  Automated 3D radiology report generation often suffers from clinical hallucinations and a lack of the iterative verification found in human practice.<n>We propose MARCH, a multi-agent framework that emulates the professional hierarchy of radiology departments and assigns specialized roles to distinct agents.<n>On the RadGenome-ChestCT dataset, MARCH significantly outperforms state-of-the-art baselines in both clinical fidelity and linguistic accuracy.
  arXiv  Detail & Related papers  (2026-04-17T15:42:03Z)
• A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice [83.11942224668127]
  Janus-Pro-CXR (1B) is a chest X-ray interpretation system based on DeepSeek Janus-Pro model.<n>Our system outperforms state-of-the-art X-ray report generation models in automated report generation.
  arXiv  Detail & Related papers  (2025-12-23T13:26:13Z)
• CXRAgent: Director-Orchestrated Multi-Stage Reasoning for Chest X-Ray Interpretation [62.0150409256153]
  We propose CXRAgent, a director-orchestrated, multi-stage agent for CXR interpretation.<n>The agent strategically orchestrates a set of CXR-analysis tools, with outputs normalized and verified by the Evidence-driven Validator.<n>Experiments on various CXR interpretation tasks show that CXRAgent delivers strong performance, providing visual evidence and generalizes well to clinical tasks of different complexity.
  arXiv  Detail & Related papers  (2025-10-24T10:31:30Z)
• AI-Driven Radiology Report Generation for Traumatic Brain Injuries [0.0]
  We propose an AI-based approach for automatic radiology report generation tailored to cranial trauma cases.<n>Our model integrates an AC-BiFPN with a Transformer architecture to capture and process complex medical imaging data.<n>We evaluate the performance of our model on the RSNA Intracranial Hemorrhage Detection dataset.
  arXiv  Detail & Related papers  (2025-10-09T17:39:04Z)
• RadFabric: Agentic AI System with Reasoning Capability for Radiology [61.25593938175618]
  RadFabric is a multi agent, multimodal reasoning framework that unifies visual and textual analysis for comprehensive CXR interpretation.<n>System employs specialized CXR agents for pathology detection, an Anatomical Interpretation Agent to map visual findings to precise anatomical structures, and a Reasoning Agent powered by large multimodal reasoning models to synthesize visual, anatomical, and clinical data into transparent and evidence based diagnoses.
  arXiv  Detail & Related papers  (2025-06-17T03:10:33Z)
• A Multimodal Multi-Agent Framework for Radiology Report Generation [2.1477122604204433]
  Radiology report generation (RRG) aims to automatically produce diagnostic reports from medical images.<n>We propose a multimodal multi-agent framework for RRG that aligns with the stepwise clinical reasoning workflow.
  arXiv  Detail & Related papers  (2025-05-14T20:28:04Z)
• CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models [1.7042756021131187]
  This paper presents an automated radiology report generation framework that combines Concept Bottleneck Models (CBMs) with a Multi-Agent Retrieval-Augmented Generation (RAG) system.<n>CBMs map chest X-ray features to human-understandable clinical concepts, enabling transparent disease classification.<n>RAG system integrates multi-agent collaboration and external knowledge to produce contextually rich, evidence-based reports.
  arXiv  Detail & Related papers  (2025-04-29T16:14:55Z)
• AutoRG-Brain: Grounded Report Generation for Brain MRI [57.22149878985624]
  Radiologists are tasked with interpreting a large number of images in a daily base, with the responsibility of generating corresponding reports. This demanding workload elevates the risk of human error, potentially leading to treatment delays, increased healthcare costs, revenue loss, and operational inefficiencies. We initiate a series of work on grounded Automatic Report Generation (AutoRG) This system supports the delineation of brain structures, the localization of anomalies, and the generation of well-organized findings.
  arXiv  Detail & Related papers  (2024-07-23T17:50:00Z)
• D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions [8.50767187405446]
  We propose D-Rax -- a domain-specific, conversational, radiologic assistance tool. We enhance the conversational analysis of chest X-ray (CXR) images to support radiological reporting. We observe statistically significant improvement in responses when evaluated for both open and close-ended conversations.
  arXiv  Detail & Related papers  (2024-07-02T18:43:10Z)
• ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data [115.0747462486285]
  ChatRadio-Valuer is a tailored model for automatic radiology report generation that learns generalizable representations. The clinical dataset utilized in this study encompasses a remarkable total of textbf332,673 observations. ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al.
  arXiv  Detail & Related papers  (2023-10-08T17:23:17Z)
• Cross-Modal Causal Intervention for Medical Report Generation [107.76649943399168]
  Radiology Report Generation (RRG) is essential for computer-aided diagnosis and medication guidance.<n> generating accurate lesion descriptions remains challenging due to spurious correlations from visual-linguistic biases.<n>We propose a two-stage framework named CrossModal Causal Representation Learning (CMCRL)<n> Experiments on IU-Xray and MIMIC-CXR show that our CMCRL pipeline significantly outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-16T07:23:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.