Radiology Guideline-derived Decision Graph for Efficient Clinical Decision Support

Xuewen Zhou; Zhen Chen

cs.CVOct 29, 2026

Radiology Guideline-derived Decision Graph for Efficient Clinical Decision Support

Authors: Xuewen Zhou, Zhen Chen

Organizations: Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University

Abstract

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.

Figures & tables

Figure 1: Overview of the GDGraph framework. (a) LLM-based Graph Construction: Offline, an LLM partitions clinical guidelines into $m$ section-based chunks $\{C_{i}\}_{i=1}^{m}$ , extracts local subgraphs $\{G_{i}\}_{i=1}^{m}$ with TF-IDF-retrieved definitions from a global memory, and aggregates related branches into a unified decision graph $G$ . (b) Graph-Guided Selection: Online, a System One model answers localized questions using case findings and attached definitions, guiding traversal along the selected path to reach a traceable decision.

1:	Partition $D$ into section-based chunks $C=\{C_{1},\dots,C_{m}\}$
2:	Build definition memory $M\leftarrow\bigcup_{i=1}^{m}\mathrm{ExtractTerms}(C_{i})$
3:	for each chunk $C_{i}\in C$ do
4:	Retrieve relevant definitions $d_{i}\subset M$ via TF-IDF
5:	Extract subgraph $G_{i}=(\mathcal{S}_{i},\mathcal{A}_{i},\mathcal{E}_{i},\mathcal{Y}_{i})$ from $(C_{i},d_{i})$
6:	Render questions for each $a\in\mathcal{A}_{i}$ with $\mathcal{D}_{a}\subset M$
7:	end for

Algorithm 1 GDGraph Construction

Guideline	Clinical Task	Cases
CUA (2023) [ 11 ]	Renal cyst management	127
O-RADS US v2022 [ 15 ]	Adnexal mass stratification	117
K-TIRADS (2021) [ 4 ]	Thyroid nodule stratification	103
Total		347

Table 1: Summary of evaluated clinical guidelines, target tasks, and case counts.

Guideline	Node Prec.	Rule Prec.
CUA	137/137 (100.0%)	51/55 (92.7%)
O-RADS	136/137 (99.3%)	92/98 (93.9%)
K-TIRADS	216/216 (100.0%)	123/123 (100.0%)
Overall	489/490 (99.8%)	266/276 (96.4%)

Table 2: Precision of constructed decision graphs against source guidelines, reported as supported/total (%).

Clinical Decision Support

Clinical Decision Support uses data, medical knowledge, and AI to help clinicians assess patients and choose tests, diagnoses, and treatments.

See all 244 papers

Guideline2Graph: Profile-Aware Multimodal Parsing for Executable Clinical Decision Graphs
Apr 2, 2026Onur Selim Kilic, Yeti Z. Gurbuz, Cem O. Yaldiz +4Clinical Decision Support Clinical Information Extraction
Georgia Institute of Technology · MetaDialog · Infuse Inc.
GuideSkill: Evolving Executable LLM Agent Skills for Guideline-Grounded Clinical Reasoning
Jul 28, 2026Lang Cao, Yuhao Shen, Tianyang Luo +3Clinical Decision Support LLM Agent Skill Learning
University of Illinois Urbana-Champaign · ♦The Chinese University of Hong Kong, Shenzhen · ⋂Albert Einstein College of Medicine
MedGuideX: Internalizing Decision Logic from Executable Guidelines into Large Language Models for Clinical Reasoning
May 26, 2026Yuhao Shen, Lang Cao, Simo Du +4Healthcare Clinical Decision Support
University of Illinois Urbana-Champaign · 2The Chinese University of Hong Kong, Shenzhen · 3Albert Einstein College of Medicine

Apr 2, 2026J/K move · Enter open · S save

Guideline2Graph: Profile-Aware Multimodal Parsing for Executable Clinical Decision Graphs

Onur Selim Kilic, Yeti Z. Gurbuz, Cem O. Yaldiz, Afra Nawar+3

Georgia Institute of Technology · MetaDialog · Infuse Inc.

Clinical Decision Support Clinical Information Extraction

Open paper AskPDF arXiv

Clinical practice guidelines are long, multimodal documents whose branching recommendations are difficult to convert into executable clinical decision support (CDS), and one-shot parsing often breaks cross-page continuity. Recent LLM/VLM extractors are mostly local or text-centric, under-specifying section interfaces and failing to consolidate cross-page control flow across full documents into one coherent decision graph. We present a decomposition-first pipeline that converts full-guideline evidence into an executable clinical decision graph through topology-aware chunking, interface-constrained chunk graph generation, and provenance-preserving global aggregation. Rather than relying on single-pass generation, the pipeline uses explicit entry/terminal interfaces and semantic deduplication to preserve cross-page continuity while keeping the induced control flow auditable and structurally consistent. We evaluate on an adjudicated prostate-guideline benchmark with matched inputs and the same underlying VLM backbone across compared methods. On the complete merged graph, our approach improves edge and triplet precision/recall from

19.6\%/16.1\%

in existing models to

69.0\%/87.5\%

, while node recall rises from

78.1\%

93.8\%

. These results support decomposition-first, auditable guideline-to-CDS conversion on this benchmark, while current evidence remains limited to one adjudicated prostate guideline and motivates broader multi-guideline validation.

Abstract

Clinical Decision Support

Explore similar work

Guideline2Graph: Profile-Aware Multimodal Parsing for Executable Clinical Decision Graphs

GuideSkill: Evolving Executable LLM Agent Skills for Guideline-Grounded Clinical Reasoning

MedGuideX: Internalizing Decision Logic from Executable Guidelines into Large Language Models for Clinical Reasoning