A Brief Review of Explainable Artificial Intelligence in Healthcare

AI transparency is critical in healthcare because wrong predictions can have severe consequences for patients. The paper emphasizes that black box AI systems—those whose decision-making processes cannot be easily understood—are particularly problematic in medical settings where clinicians need to understand the reasoning behind AI recommendations before trusting and implementing them.

Multiple explainability methods exist, each serving different purposes. The review identifies six categories of XAI approaches (features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods), suggesting that no single solution fits all healthcare AI applications and different methods may be appropriate for different clinical scenarios.

Clinician trust depends on understanding AI reasoning, not just accuracy. The paper stresses that for successful deployment of AI in healthcare practices, the underlying reasoning of AI models must be transparent to medical professionals—accuracy alone is insufficient if doctors cannot understand how the AI reached its conclusions.

Significant challenges remain in implementing explainable AI in healthcare. While the paper identifies various XAI methods and their limitations, it indicates that research gaps persist in making these techniques practical and effective for real-world clinical deployment.