Evidence-Intensive Clinical Reasoning With Knowledge Graphs for Epilepsy Decision Support

Abstract

Epilepsy diagnosis and treatment require reasoning across heterogeneous evidence, including seizure semiology, electroencephalography, neuroimaging, genetics, medication response, and clinical guidelines. This article proposes a knowledge graph-based framework for evidence-intensive epilepsy decision support. The framework organizes epilepsy-related clinical concepts, patient observations, diagnostic categories, treatment options, and evidence sources into a structured graph representation. Large language models are used as reasoning interfaces, while graph retrieval provides grounded medical evidence to reduce hallucination and improve traceability. The framework also draws on recent advances in medical AI, neurodegenerative disease knowledge mapping, cognitive assessment review, transcriptomic regulation, ionic-stress sensing, and multimodal medical imaging. It supports tasks such as seizure classification, differential diagnosis, treatment recommendation, cognitive evaluation assistance, and explanation generation. The article emphasizes that clinical AI systems should not act as autonomous diagnostic agents but should provide evidence-linked support for clinicians. By combining knowledge graphs, retrieval-augmented reasoning, and explainable clinical decision support, the proposed approach addresses the need for reliable AI tools in complex neurological conditions.