Salaudeen et al. review common reliability failures in predictive and generative AI for healthcare, including erroneous model outputs, clinically unjustified performance differences, and deployment-time degradation. They examine why existing technical solutions fall short and argue for lifecycle-aware evaluation, continuous monitoring, and institutional governance.