---
title: "Radiology Guideline-derived Decision Graph for Efficient Clinical Decision Support"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2610.40327v1"
permalink: "https://feed.astralor.com/items/itm_ec66286eeb3678b7a4377b2f"
published: "2026-10-29T15:56:27.000Z"
score: 54
channel: "tech"
tags: ["LLM","OpenAI","推理效率","RAG","Radiology Guideline-derived Decision","Graph","Efficient Clinical Decision","Support Radiological"]
---

# Radiology Guideline-derived Decision Graph for Efficient Clinical Decision Support

## At a glance

Radiological risk stratification and management planning are essential downstream tasks in medical imaging. Automating these tasks is challenging because clinical guideline criteria are distributed across prose, tables,…

## Key points

- Radiological risk stratification and management planning are essential downstream tasks in medical imaging.
- Automating these tasks is challenging because clinical guideline criteria are distributed across prose, tables, and cross-referenced definitions.
- We present Guideline-derived Decision Graph (GDGraph), a framework that decouples guideline compilation from case evaluation.

## Full text

Radiological risk stratification and management planning are essential downstream tasks in medical imaging. Automating these tasks is challenging because clinical guideline criteria are distributed across prose, tables, and cross-referenced definitions. We present Guideline-derived Decision Graph (GDGraph), a framework that decouples guideline compilation from case evaluation. During compilation, a large language model partitions guidelines into section-based chunks, extracts local decision subgraphs with linked definitions, and aggregates related branches into an executable decision graph. During inference, a fast System One model answers localized clinical questions while deterministic traversal resolves Boolean logic, turning opaque single-pass predictions into a traceable decision path without task-specific training. Across 347 cases spanning renal cyst, adnexal mass, and thyroid nodule guidelines, GDGraph improves System One accuracy from 57.3% to 90.8%, outperforming retrieval-augmented GPT-4o (80.1%) and graph-guided GPT-4o with chain-of-thought (82.7%) at substantially lower latency and cost. The source code will be available.

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