Internal Validation with Bayesian models

In the clinical prediction model framework, many suggest doing internal validation after model development to quantify and adjust for optimism/overfitting. The most recommended procedure is doing non-parametric bootstrapping 200-500 times. However, doing such a procedure with a Bayesian model and a large dataset is infeasible due to computational burden. In the Bayesian literature, model checking with posterior predictive checks and even leave-one-out cross-validation are commonly recommended. Yet I have not yet seen these procedures in the clinical prediction literature. How should one internally validate a Bayesian clinical prediction model?