A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?

Explainability is critical for medical AI adoption: The paper emphasizes that healthcare professionals and patients need to understand why AI systems make specific clinical decisions, not just accept their predictions. This transparency is essential for building trust in AI-assisted diagnosis and treatment recommendations.

Multiple XAI methods exist but lack standardization: The review identifies various explainability techniques across the literature (1) but notes that there is no single standardized approach. Healthcare organizations must carefully evaluate which methods work best for their specific clinical applications and stakeholder needs.

"When" to use XAI matters as much as "how": The paper stresses that explainability requirements vary depending on the clinical context—high-stakes decisions like cancer diagnosis require more rigorous explanation than lower-risk applications, suggesting a risk-based approach to implementing XAI in healthcare.

Trustworthy AI requires more than just explanation: The review connects XAI to broader trustworthiness in healthcare AI, indicating that explainability alone is insufficient; it must be paired with validation, accountability mechanisms, and alignment with clinical workflows to ensure responsible deployment.

The field is still formalizing best practices: As a systematic review covering 2012-2022, the paper documents that XAI in healthcare remains an evolving area without fully established standards, meaning healthcare institutions implementing AI systems should expect ongoing refinement of explainability requirements and methods.