The application of explainable artificial intelligence in the prediction, diagnoses, treatment, and management of chronic diseases: A systematic review
Hooman Hoghooghi Esfahani, Shogo Toyonaga, Kiemute Oyibo
Abstract
Study objectives: This systematic review analyzes the applications of explainable artificial intelligence (XAI) algorithms in chronic disease care, focusing on prediction, diagnosis, treatment, and management. The study examines prevalent XAI approaches across different chronic conditions and evaluates research gaps. Methods: The review followed Preferred Reporting Items for Systematic Review and Meta-analysis 2020 guidelines, analyzing relevant articles from 6 databases to identify and evaluate XAI implementations in chronic disease care. A protocol for this systematic review was not registered anywhere prior to publication. Results: Three primary XAI techniques emerged as dominant: SHapley Additive exPlanations (SHAP) (46.5%), Local Interpretable Model-Agnostic Explanations (25.8%), and Gradient-weighted Class Activation Mapping (Grad-CAM) (12.0%). Disease prediction dominated the applications (86.2%), with SHAP being preferred for structured clinical data and Grad-CAM showing strength in medical imaging. Implementation varied significantly across different chronic conditions, with standardized diagnostic criteria and structured data receiving more attention. Discussion: The analysis revealed an imbalance in healthcare applications, with sophisticated prediction models but limited treatment planning and disease management implementations. Key challenges included insufficient handling of complex multimodal data types and limited data volume. The need for extensive clinical validation in real-world settings was identified as crucial for establishing practical utility. Conclusion: While XAI shows promise in chronic disease healthcare, advancement requires expanding beyond prediction into treatment and management domains, developing robust approaches for complex medical data, and implementing larger-scale studies. Success depends on collaboration between AI researchers, healthcare professionals, legal experts, and policymakers, alongside clear regulatory guidelines and governance frameworks balancing innovation with patient privacy.