arXiv (All)
open access
MAC-RRG: Iterative Multi-Agent Collaboration for X-ray Radiology Report Generation
Despite the remarkable progress of LLM-based and knowledge graph-augmented Radiology Report Generation (RRG) methods, existing techniques still suffer from inherent defects. Conventional LLM-only models lack structured medical prior knowledge, resulting in frequent medical hallucinations and low dia
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Integrating knowledge from case reports: a medical ontology based multimodal information system with structured summary#1014361
- Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods#974197
- Feedback-Robust AI for Patient Knowledge Graphs#1117960
- A Case-Driven and Knowledge Graph-Supported Approach to Pathology Education: Effects on Knowledge Retention and Clinical Reasoning.#785435
- Ontological Support for Living Plan Specification, Execution and Evaluation#772860
Record · ID 1062027
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive