Explainable AI in Cardiology Diagnostics: A Systematic Review of Machine Learning, Meta-heuristic Optimization, and...

This systematic review compiles evidence and examines how various artificial intelligence (AI) approaches, including machine learning (ML), natural language processing (NLP), meta-heuristic optimization, and explainable AI (XAI), are utilized to predict and diagnose coronary artery disease (CAD). We aim to identify the most commonly used models, evaluate their performance, and explore how interpretability and optimization enhance their usefulness in clinical practice.

A thorough search was conducted across five major databases (PubMed, Scopus, IEEE Xplore, ACM Digital Library, and SpringerLink) to identify relevant studies published between January 2022 and August 2025, in accordance with the PRISMA guidelines. Dual independent reviewers performed study selection and data extraction. The quality of the included studies was evaluated using a checklist based on QUADAS-2. Data were collected on study characteristics, model types, validation methods, and performance metrics, which will be the cornerstone of the analysis.

Sixty-one studies met the inclusion criteria. ML and deep learning models demonstrated strong performance and achieved high accuracy in benchmark datasets, but showed limited clinical validation. Transformer-based models (e.g., BioBERT, ClinicalBERT) showed high efficacy for medical text analysis, but require substantial data and computational resources. Meta-heuristic algorithms (e.g., Genetic Algorithms, Particle Swarm Optimization) effectively improved model efficiency but were rarely applied to unstructured clinical narratives. XAI tools (e.g., SHAP, LIME) improved model transparency, though most studies highlight a need for more rigorous evaluation.

Integrated ML, NLP, meta-heuristic optimization, and XAI hold significant promise in advancing the diagnosis of CAD by improving both accuracy and interpretability. However, challenges such as data scarcity, limited external validation, and a lack of standardized, clinician-centric explainability impede clinical adoption. Future research should focus on hybrid frameworks validated for large, diverse, and real-world datasets.