In a prospective Vietnamese cohort, explainable machine learning could rank in-hospital acute heart failure mortality risk, but it did not significantly outperform a conventional clinical risk score. The modeling framework required recalibration to align with local performance. The report indicates that even when interpretability techniques are applied, gains may be constrained by local data characteristics and differences in baseline risk. The need for recalibration suggests that deployment without ongoing model maintenance could reduce reliability. For digital therapeutics and AI-in-clinical-decision support teams, the results reinforce a practical requirement: prospective validation paired with ongoing calibration work. Regulatory acceptance will likely depend on demonstrating both discrimination and calibration stability across settings.