Interpretability in Healthcare ML: Why Black-Box Models Struggle in Practice

In many machine learning applications, model performance is the primary objective.

However, healthcare presents a different challenge.

Here, model adoption depends not only on performance, but also on interpretability and trust.

Clinicians must be able to understand and justify decisions, especially in high-risk environments.

This creates limitations for black-box models.

Even when they achieve strong predictive performance, they may not be used if their outputs are difficult to interpret.

Key requirements for healthcare ML systems include:

• Transparent reasoning behind predictions
• Alignment with clinical workflows
• Consistent and reliable outputs
• Ability to support decision-making under uncertainty

Interpretability techniques such as feature importance, SHAP values, and model simplification can help address this challenge.

However, technical solutions alone are not sufficient.

Interpretability must also align with how clinicians think and make decisions.

This highlights an important shift:

Healthcare ML is not just about optimizing models.

It is about designing systems that are understandable and usable in real-world environments.

My work focuses on applying machine learning with this broader perspective — ensuring that models are both effective and interpretable.

I am open to remote roles globally.

Follow my work here:

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