{"slug": "monteret-ai-agent-enhancing-multimodal-llms-with-multi-granularity-knowledge-for", "title": "MonteRET: AI Agent Enhancing Multimodal LLMs with Multi-granularity Knowledge Retrieval for Chest CT Report Generation", "summary": "Researchers developed MonteRET, a region-aware retrieval-enhanced framework that uses an AI agent to generate chest CT reports by integrating global and region-level anatomical features. In evaluations on 24,128 CT scans and external tests, MonteRET outperformed state-of-the-art methods in report quality, semantic similarity, and clinical efficacy, with fewer omitted findings. The system was preferred by radiology residents in human expert evaluations.", "body_md": "arXiv:2607.14264v1 Announce Type: new\nAbstract: Automated chest CT report generation remains challenging because clinically faithful reporting requires both whole-volume understanding and accurate description of localized anatomical findings. Here we developed and retrospectively evaluated MonteRET, a region-aware retrieval-enhanced framework for generating chest CT findings sections. MonteRET integrates global CT features with region-level anatomical representations, retrieves clinically relevant knowledge using predicted medical conditions and region-level vision-language alignment, and refines initial reports through a knowledge-guided report rewriting agent. We trained our model on a public cohort with 24,128 CT scans from RadGenome-ChestCT. We evaluated MonteRET on the public RadGenome-ChestCT test set of 1,564 CT scans and an external cohort of 82 CT scans from NewYork-Presbyterian/Weill Cornell Medical Center. MonteRET improved report quality, semantic similarity, and clinical efficacy compared with a matched baseline and several state-of-the-art methods. Gains were most pronounced for recall, suggesting fewer omitted findings. Human expert evaluation by radiology residents also favored MonteRET.", "url": "https://wpnews.pro/news/monteret-ai-agent-enhancing-multimodal-llms-with-multi-granularity-knowledge-for", "canonical_source": "https://arxiv.org/abs/2607.14264", "published_at": "2026-07-17 04:00:00+00:00", "updated_at": "2026-07-17 04:07:49.119589+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "ai-research", "computer-vision", "natural-language-processing"], "entities": ["MonteRET", "RadGenome-ChestCT", "NewYork-Presbyterian", "Weill Cornell Medical Center"], "alternates": {"html": "https://wpnews.pro/news/monteret-ai-agent-enhancing-multimodal-llms-with-multi-granularity-knowledge-for", "markdown": "https://wpnews.pro/news/monteret-ai-agent-enhancing-multimodal-llms-with-multi-granularity-knowledge-for.md", "text": "https://wpnews.pro/news/monteret-ai-agent-enhancing-multimodal-llms-with-multi-granularity-knowledge-for.txt", "jsonld": "https://wpnews.pro/news/monteret-ai-agent-enhancing-multimodal-llms-with-multi-granularity-knowledge-for.jsonld"}}