Interpretability

Why does interpretability matter in healthcare specifically?

In healthcare, interpretability is central to clinical trust. A clinician who is asked to act on an AI-generated recommendation (for example a diagnosis, a risk score, or a treatment suggestion) needs to understand the basis for that recommendation before incorporating it into patient care.

This matters for patient autonomy: patients have a right to understand how decisions about their care are being made, and clinicians need to be able to explain and justify those decisions. It also matters for accountability: if an algorithm makes an error, interpretability helps identify what went wrong and why, information that's essential for correcting the issue and for any subsequent regulatory or legal review.

What's the difference between "interpretability" and "explainability"?

The terms are often used interchangeably, but there's a subtle distinction worth noting:

• Interpretability generally refers to the intrinsic transparency of a model, how directly a human can understand its internal workings just by inspecting it, without needing any additional tools.
• Explainability typically refers to techniques used to generate an understandable explanation for a model's output after the fact, particularly useful for inherently complex "black box" models, like deep neural networks, whose internal workings are not directly interpretable on their own.

In practice, many AI systems in healthcare rely on explainability techniques precisely because the underlying model itself isn't naturally interpretable.

What are some common techniques for improving interpretability?

Several approaches are commonly used to make AI models, or their outputs, more interpretable:

• Feature importance methods - techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations), which estimate how much each input feature contributed to a specific prediction.
• Attention mechanisms - used in some deep learning models to highlight which parts of the input (e.g., a specific region of a medical image) the model "focused on" most when making its decision.
• Multiple instance learning (MIL) - an approach often used in digital pathology, where a model makes predictions from a whole slide image while also identifying which specific regions of the tissue most influenced its conclusion, offering pathologists a way to check the model's reasoning against their own.
• Surrogate models - using a simpler, inherently interpretable model to approximate and explain the behaviour of a more complex "black box" model.
• Visualisation tools - such as heatmaps overlaid on medical images, showing which pixels or regions most influenced a model's classification.

What are the trade-offs involved in building interpretable models?

There's often a trade-off between interpretability and predictive performance. Simpler, more interpretable models (like decision trees or logistic regression) can be easier to understand and validate, but may not capture the same level of complex, non-linear relationships in the data that a deep neural network can.

More complex models often achieve higher accuracy, particularly for tasks like image analysis, but at the cost of being harder to interpret directly. This is why explainability techniques, like feature attribution or attention mechanisms, have become so important: they attempt to preserve the performance benefits of complex models while still providing clinicians and researchers with some insight into how those models are reasoning.

Is a lack of interpretability a reason to avoid using an AI model?

Not necessarily, but it should factor into how and where a model is deployed. A highly accurate but poorly interpretable model might still be appropriate for lower-stakes research or exploratory tasks. For high-stakes clinical decisions, however, where a clinician needs to explain a recommendation to a patient, or where regulatory approval requires evidence of why a model reaches its conclusions, interpretability becomes a much more important consideration, sometimes as important as raw accuracy itself.

This is why interpretability is increasingly treated as a core requirement in the validation process for medical AI, alongside accuracy, robustness, and fairness.