The usual methods to replicate clinical trials primarily relied on deterministic models and simple probabilistic approaches to replicate trial circumstances and evaluate prospective outcomes ( ). More complex models that consider patient demographics, illness development, and therapeutic effects can be produced as computer capability develops (Figure ). By predicting anticipated difficulties and improving trial procedures, clinical trial simulations have helped to raise the ethical standards and efficiency of trial design. These approaches created the basis for the present advanced simulations, which today incorporate sophisticated algorithms and machine learning techniques to produce even more exact and dynamic forecasts ( ), even if they have some constraints. Model A suggests using in-brace corrections for patients in group B, while Model B predicts the impact of these suggestions. The predicted results were compared to the actual outcomes recorded in the charts of patients in group B ( ).