Comprehensive Survey of Retrieval-Augmented, Knowledge-Graph, and Multimodal Large Language Models for Inclusive Healthcare Guidance: Architectures, Benchmarks, and Clinical Deployment

Abstract

This paper surveys and compares Retrieval-Augmented Generation (RAG), Knowledge-Graph (KG) en-hanced reasoning, and Multimodal Large Language Models (LLMs) within medical contexts. Beyond model accuracy, it emphasizes inclusivity, multilingual access, and doctor-in-the-loop feedback. Synthesizing 41 key publications (2018–2025), it covers architectures, datasets, reasoning mechanisms, safety metrics, and future challenges. The goal is to guide design of transparent, clinically validated, and equitable AI systems deployable in low-resource healthcare settings

Introduction

This survey reviews the evolution and advancement of Medical Large Language Models (LLMs) in healthcare, focusing on overcoming challenges such as factual hallucinations, outdated knowledge, weak integration of structured medical information, and poor support for low-resource languages. The study analyzes 41 research papers (2018–2025) from major scientific databases and examines developments in retrieval-based systems, knowledge graph reasoning, multimodal learning, and multilingual healthcare AI.

The evolution of medical LLMs is divided into four stages. The first stage involved domain-specific models such as BioBERT and ClinicalBERT, which improved biomedical language understanding. The second stage introduced instruction-tuned conversational models like ChatDoctor and BioInstruct, enabling more natural medical dialogue. The third stage focused on Retrieval-Augmented Generation (RAG) and Knowledge Graph (KG) integration, significantly reducing hallucinations and improving factual accuracy. The latest stage emphasizes multimodal and multilingual systems, combining medical images, clinical data, and multiple languages to improve healthcare accessibility and diagnostic capabilities.

The survey categorizes medical LLMs into five major families: Text-only LLMs, RAG-based models, Knowledge Graph-augmented models, Multimodal models, and Multilingual models. Each offers distinct advantages, such as faster inference, improved factual grounding, explainable reasoning, image-text understanding, and broader linguistic coverage.

Benchmark analysis shows that KG-augmented models achieved the highest accuracy (91.2%) and lowest hallucination rate (5.3%), while RAG-based models reduced hallucinations by nearly 60–70% compared to traditional text-only LLMs. Multimodal systems improved medical image understanding and visual question answering performance by approximately 6–9%, while multilingual models enhanced healthcare accessibility despite challenges caused by limited training data.

The study highlights the growing importance of hybrid architectures that combine RAG, Knowledge Graph reasoning, and multimodal learning. These systems use retrieval modules for evidence-based responses, knowledge graphs for logical and explainable reasoning, and multimodal encoders for integrating medical images, clinical records, and other healthcare data. Such integration improves factual accuracy, transparency, and clinical decision support.

The survey also discusses interpretability and trustworthiness, emphasizing techniques such as Chain-of-Thought reasoning, attention visualization, token-level explanations, and causability metrics to ensure AI decisions align with valid medical reasoning. Additionally, multi-agent frameworks are introduced, where specialized agents perform retrieval, analysis, synthesis, verification, and bias auditing to create more reliable and traceable healthcare AI systems.

Conclusion

Retrieval-Augmented Generation (RAG), Knowledge Graph (KG) reasoning, and multimodal learning represent the next major stage in the evolution of medical artificial intelligence systems. These approaches combine factual information retrieval, structured medical reasoning, and multimodal perception to create intelligent healthcare assistants capable of supporting clinical diagnosis, medical decision-making, and patient communication. By incorporating fairness, multilingual accessibility, and continuous clinical oversight, such systems have the potential to provide more equitable and reliable digital healthcare support across diverse populations and geographic regions. Future research should focus on standardized evaluation frameworks, robust data governance mechanisms, and improved support for low-resource languages to achieve the vision of globally accessible and clinically validated AI-driven healthcare

References

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Copyright

Copyright © 2026 Pratik J. Mali, Rucha A. Kulthe, Laxmi G. Ughade. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.