A healthcare organisation wants to choose an AI model type that is inherently interpretable so that clinicians can understand the decision logic without additional tools. Which model type best meets this requirement?
- AA large transformer-based language model fine-tuned on medical records.
- BA deep convolutional neural network trained on medical imaging data.
- CA random forest ensemble of hundreds of decision trees trained on patient data.
- DA shallow decision tree with a limited number of nodes and leaves. Correct
Why A is wrong: Large transformer models are highly capable but are considered black-box systems; their decision logic cannot be directly inspected by clinicians without separate explainability tooling.
Why B is wrong: Deep CNNs are powerful for imaging tasks but their layered weight matrices are not inherently human-readable, making them black-box models that require post-hoc interpretation techniques.
Why C is wrong: Although individual decision trees are interpretable, a random forest averages predictions across hundreds of trees, making the combined logic opaque. It shares the black-box property of other ensemble methods.
Why D is correct: A shallow decision tree represents its logic as a visible sequence of if-then rules that clinicians can read and verify directly. This is the canonical example of an inherently interpretable model, requiring no additional explanation tools.