AN ENSEMBLE EXPLAINABILITY FRAMEWORK FOR MULTIMODAL CHEST X-RAY DISEASE - Haruna, Dahiru Usman; Hassan, Abubakar Sadiq; Mohammed, Idi (FUDMA Journal of Sciences, 2026)
This systematic literature review examines ensemble explainability frameworks for multimodal Chest X-ray (CXR) classification using SHAP, Grad-CAM, and LIME. Following PRISMA 2020 guidelines, we searched Scopus, Google Scholar, PubMed, and arXiv (2016-2025), identifying 945 records and including 30 high-quality papers after rigorous screening. Findings reveal a significant trend toward multimodal architectures combining imaging with clinical parameters, electronic health records, and expert annotations. Grad-CAM dominates as a visualization tool (97% of studies) for localizing pathological features, while SHAP and LIME are increasingly used for model-agnostic feature attribution. However, true ensemble frameworks integrating all three methods remain rare (13%). High-performing multimodal systems achieved AUROCs of 0.82-0.99 for mortality prediction and 0.85-0.96 for disease classification. Critical gaps include: (1) lack of standardized XAI validation protocols; (2) inconsistent reporting of metrics and datasets; (3) limited external validation; and (4) insufficient comparative analysis of XAI methods. This review synthesizes current methodologies and proposes future directions for developing interpretable AI systems in chest radiograph analysis.