Model interpretability is crucial for several reasons. Firstly, it enhances trust in predictions. Clinicians are more likely to rely on model outputs if they understand the reasoning behind them. Secondly, interpretability can help identify potential biases or errors in the model, which is essential for ensuring fairness and accuracy in patient care. Lastly, it provides insights into biological mechanisms, potentially unveiling new pathways or therapeutic targets in cancer treatment.
