Integrating LLMs and Knowledge Graphs for Medical AI: Advances, Challenges, and Future Directions.

Abstract

This review synthesizes how integrating large language models (LLMs) with knowledge graphs (KGs) advances medical AI across methods, applications, and evaluation. While LLMs excel at natural language understanding and contextual reasoning, KGs provide structured factual knowledge, ensuring reliability in critical domains like healthcare AI. This review explores recent advances, emphasizing how LLM-KG synergy enhances knowledge extraction, clinical decision support, and explainability in medical applications. We analyze integration methodologies across three key frameworks: (a) KG-enhanced LLMs, where KGs refine reasoning during pre-training and inference; (b) LLM-augmented KGs, where LLMs improve KG construction, reasoning, and query resolution; and (c) Synergistic LLM-KG systems, which enable bidirectional knowledge exchange for more robust AI-driven decision-making. While these models offer substantial improvements in medical diagnostics, personalized treatment, and automated knowledge discovery, key challenges remain. Issues such as data heterogeneity, reasoning transparency, computational scalability, and ethical considerations surrounding patient data must be addressed to enable real-world clinical adoption. This review outlines future directions, including cross-domain knowledge integration, neurosymbolic AI frameworks, causal reasoning for explainable predictions, and multi-agent ensemble models for adaptive decision-making. We emphasize that scalability, real-time KG updates, and privacy-preserving mechanisms are vital for responsible, high-impact AI deployment in medicine.