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October 16, 2025Open Access

DKG-LLM : A Framework for Medical Diagnosis and Personalized Treatment Recommendations via Dynamic Knowledge Graph and Large Language Model Integration

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DKG-LLM achieves a diagnostic accuracy of 84.19%, demonstrating significant potential in medical applications.
Using real-world datasets, the framework successfully integrates a dynamic knowledge graph for better data management.
The adaptive semantic fusion algorithm enables scalable updates, with maintenance of up to 987,654 edges in the knowledge graph.
This tool effectively addresses complex multi-symptom diseases, improving treatment recommendations with an accuracy of 89.63%.

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This framework integrates a dynamic knowledge graph and language model, achieving 84.19% diagnostic accuracy and 89.63% in treatment recommendations.

Sarabadani et al. (2025) studied this question.

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This framework integrates a dynamic knowledge graph and language model, achieving 84.19% diagnostic accuracy and 89.63% in treatment recommendations.