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This is a Systematic Review study.

A Comprehensive Review of Explainable AI (XAI) Methods in Deep Learning

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Explainable AI techniques enhance transparency in deep learning, critical for high-stakes domains like healthcare.
Core explanation methods include local surrogate modeling of LIME and game-theoretic frameworks such as SHAP.
A detailed taxonomy categorizes XAI methods into intrinsic versus post-hoc and model-agnostic versus model-specific.
Future directions highlight the need for standardized benchmarks and the pursuit of causal explanations over mere correlations.

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This review surveys explainable AI (XAI) methods in deep learning, emphasizing healthcare and finance applications.

Thakur et al. (2025) studied this question.

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