The application of explainable artificial intelligence in the prediction, diagnoses, treatment, and management of chronic diseases: A systematic review.

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 Explana

SUBMITTER: Hoghooghi Esfahani H 

PROVIDER: S-EPMC12647564 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature