Integrating Explainable AI and Graph Neural Networks for Disease Prediction: A Comprehensive Review of EHR-based Healthcare Models

Abstract

Due to Artificial Intelligence (AI) and Machine Learning (ML), the Healthcare sector has increased rapidly and progressed in disease prediction, diagnosis and chronic disease management. But these technologies have not been fully utilized because of many advanced models as they are difficult to understand. And to overcome all this, Explainable Artificial Intelligence (XAI) has been developed. That makes the model easy to understand and makes decisions. Also, help to increase the trust of doctors and patients. Addition to this, the Graph Neural (GNNs) and Graph Convolutional Networks (GCNs) such as graph-based methods, Electronic Health Records (EHR) have complex relational ships for understanding them better. In this review of 2024-2026 the 20 studies have been covered. That contains heart disease, diabetes, cancer and mental health, etc. SHAP, LIME, and Attention mechanism techniques make explainability better. Whereas, due to lack of data, real world validations and high computational cost remain.