Interpretable vs. Post-Hoc Explained Models for Digital Twin Applications in Clinical Forecasting: A Multiple Sclerosis Case Study

The growing demand for transparent and trustworthy Artificial Intelligence (AI) in healthcare is increasing the focus on the in-terpretability of predictive models, a crucial aspect for the realization of precise Digital Twins (DTs) for health. In this study, we compare intrinsically interpretable models with black-box solutions supported by post-hoc explanation techniques, aiming to improve the predictive capacity of DTs regarding the progression of Multiple Sclerosis (MS). We explore different typologies of models, such as logistic regression and neural networks, and evaluate their predictions using specific metrics for unbalanced clinical datasets, typical of DT simulations. We analyse feature attribution with a special emphasis on the comparison between native interpretability and explanations derived via SHAP (SHapley Additive Explanations), in order to validate the reliability of explanatory analyses in complex DTs. Preliminary results suggest that post-hoc methods can produce feature importance profiles aligned with those of interpretable models. This supports the use of post-hoc methods when greater model complexity is required within a DT architecture. This work highlights the practical considerations involved in balancing model performance and interpretability for decision support in longitudinal clinical settings, a critical aspect for the validation and effective use of digital twins in MS management.