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Explainable AI (XAI) in Healthcare: Trust, Transparency, and Limits

An evergreen guide explaining what Explainable AI (XAI) is, how it is applied in healthcare, why it matters for trust and transparency, and its current limitations.
8 February 2026 by
Suraj Barman

What is Explainable AI (XAI)?

Explainable AI (XAI) refers to methods and techniques that make the behavior of artificial intelligence systems understandable to humans.

  • Provides insight into model decisions
  • Enables validation of predictions
  • Supports regulatory compliance

How does XAI work in healthcare?

In healthcare, XAI techniques are applied to clinical models to reveal the rationale behind diagnoses, treatment recommendations, and risk assessments.

  • Feature importance (e.g., SHAP, LIME)
  • Rule‑based surrogate models
  • Attention maps for imaging
  • Counterfactual explanations

Why is XAI important in healthcare?

Trust, safety, and ethical considerations make transparency essential.

  • Clinician confidence in AI‑assisted decisions
  • Patient consent and informed decision‑making
  • Regulatory requirements (e.g., FDA, GDPR)
  • Detection of bias and model failures

Limitations and Challenges

Despite advances, XAI faces practical constraints.

  • Trade‑off between accuracy and interpretability
  • Complexity of deep learning models
  • Lack of standardized evaluation metrics
  • Potential for misleading explanations