MonteRET: AI Agent Enhancing Multimodal LLMs with Multi-granularity Knowledge Retrieval for Chest CT Report Generation

2026-07-15 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Medical AI · Depth: Expert, quick

Summary

MonteRET is a novel AI agent designed to enhance multimodal large language models for automated chest CT report generation. This region-aware, retrieval-enhanced framework addresses the challenge of clinically faithful reporting by integrating global CT features with region-level anatomical representations. It retrieves clinically relevant knowledge using predicted medical conditions and region-level vision-language alignment, then refines initial reports through a knowledge-guided rewriting agent. MonteRET was trained on 24,128 CT scans from RadGenome-ChestCT and evaluated on a 1,564-scan test set from the same cohort, plus an external set of 82 CT scans from NewYork-Presbyterian/Weill Cornell Medical Center. The system demonstrated improved report quality, semantic similarity, and clinical efficacy, with notable gains in recall, and was favored by human expert radiologists.

Key takeaway

For AI Scientists and Machine Learning Engineers developing automated medical report generation systems, MonteRET demonstrates a critical path forward. You should prioritize integrating multi-granularity knowledge retrieval and agent-based report rewriting to significantly improve clinical accuracy and reduce omitted findings. Consider implementing region-level vision-language alignment to enhance semantic similarity and recall in your diagnostic reporting models.

Key insights

MonteRET enhances CT report generation by integrating multi-granularity knowledge retrieval with LLMs for improved clinical accuracy.

Principles

• Combine global and region-level features.
• Use predicted conditions for knowledge retrieval.
• Refine reports with a knowledge-guided agent.

Method

MonteRET integrates global CT features with region-level anatomical representations, retrieves knowledge via predicted medical conditions and vision-language alignment, then refines reports using a knowledge-guided rewriting agent.

In practice

• Apply region-aware retrieval to medical imaging.
• Integrate vision-language models for clinical context.
• Use agent-based refinement for report accuracy.

Topics

• AI Agents
• Multimodal LLMs
• Knowledge Retrieval
• Chest CT Report Generation
• Medical Imaging
• Vision-Language Models
• RadGenome-ChestCT

Best for: NLP Engineer, Computer Vision Engineer, AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.