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Multimodal Knowledge Graph-Guided RAG-LLM for Clinical Decision Support in Pediatric Leukemia.

Jong Keon Song1, Dong Bin Youk1, Hyery Kim1

  • 1Department of Pediatrics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.

Cancer Research and Treatment
|April 23, 2026
PubMed
Summary
This summary is machine-generated.

A new multimodal retrieval-augmented generation (RAG) framework using knowledge graphs enhances clinical decision support for pediatric acute leukemia. This system offers improved accuracy and can integrate updated medical guidelines, outperforming standard large language models in expert evaluations.

Keywords:
AcuteArtificial intelligenceClinicalDecision support systemsInformation storage and retrievalLeukemiaNatural language processing

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Medicine
  • Pediatric Oncology

Background:

  • Clinical decision support systems (CDSS) are crucial for managing complex pediatric hematologic malignancies.
  • Existing CDSS often lack the ability to integrate diverse data types or rapidly adapt to evolving medical knowledge.
  • Large language models (LLMs) show promise but may struggle with evidence-dependent queries and require extensive retraining for updates.

Purpose of the Study:

  • To develop and evaluate a multimodal, knowledge graph-guided retrieval-augmented generation (RAG) framework for clinical decision support in pediatric acute leukemia.
  • To assess the performance of the RAG system against a leading LLM (GPT-4.5) using expert and LLM-based evaluations.
  • To determine the feasibility and added value of integrating multimodal data and knowledge graphs into a RAG system for specialized medical domains.

Main Methods:

  • Authoritative pediatric hematology-oncology textbooks were processed into text, tables, and figures.
  • A multimodal LLM converted visual and tabular data into structured textual descriptions.
  • A biomedical knowledge graph was constructed using LightRAG with specific embeddings (gpt-oss-20b, Qwen3).
  • The RAG system and GPT-4.5 generated responses to 10 clinical questions, evaluated by 9 medical experts and 2 LLM evaluators.

Main Results:

  • The knowledge graph contained 10,062 nodes and 15,876 edges.
  • In blind expert evaluations, the RAG system was preferred over GPT-4.5 in 47.8% of comparisons (vs. 35.6%), with significantly higher completeness scores (p=0.016).
  • RAG demonstrated a significant advantage in defining ETP-ALL immunophenotype (p=0.016).
  • LLM-based evaluations consistently favored RAG, with Claude Sonnet 4.5 preferring RAG in 6/10 questions and Gemini 3 in 7-9/10 questions.

Conclusions:

  • Multimodal, graph-based RAG is a feasible approach for clinical decision support in pediatric leukemia.
  • The RAG system offers complementary strengths to foundation LLMs, particularly for evidence-dependent queries.
  • RAG's ability to incorporate updated guidelines without retraining is a significant advantage in rapidly evolving medical fields.
  • Further validation concerning privacy and regulatory compliance is necessary before clinical implementation.