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This review investigates the use and evaluation of Explainable AI (XAI) applications in healthcare settings.

June 19, 2026AlgorithmsOpen Access

Explainable AI Applications in Healthcare: A Systematic Review

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Systematic review evaluates explainable AI's impact on healthcare, highlighting its benefits and barriers to adoption.

This review investigates the use and evaluation of Explainable AI (XAI) applications in healthcare settings.
Systematic review of 76 studies published from 2020 to 2025
Focus on operational evaluation metrics for explainability like faithfulness and trustworthiness
Analysis of XAI models used for structured data and medical imaging applications.
XAI enhances clinician trust but explanations vary across data modalities and settings
Study identifies major barriers to adoption including regulatory compliance issues and interpretability inconsistencies
There is a need for standardization in XAI validation processes to improve real-world application.

Eje et al. (2026) studied this question.

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