Why Interpretability Is Critical in Healthcare ML Systems

In healthcare ML, model performance is only part of the equation.

Interpretability is equally important.

The Problem

Black-box models provide predictions without explanation.

This creates challenges in:

validation
debugging
decision-making
Why It Matters

Clinicians need to understand:

why a prediction was made
which features influenced it
how reliable it is
Practical Approaches
SHAP values for feature attribution
LIME for local explanations
simpler interpretable models where appropriate
hybrid systems combining rules and ML
Key Insight

Interpretability is not just about transparency.

It is about enabling trust and real-world use.

I am open to remote roles globally.