Needle placement errors can mitigate the effectiveness of the diagnosis or the therapy, sometimes with catastrophic outcomes. Previous design of a simplified model for needle deflection estimation was motivated by the clinical constraints of ARCS (Abdomino-pelvic Robotic-driven slightly flexible needle insertion performed in CT/MRI-guided Scenario). We present in this work, the validation results for the needle deflection prediction model. Its robustness is evaluated under an unknown context such as a different robotic platform, facing uncertainties conditions not conceived previously in the model's confection. In addition, the work presents the development and validation experiments of an adaptive path planner that uses the model as predictor's strategy. It provides pre-operative planning assistance, as well as intra-operative decision-making support. The experiments results showed average error around 1mm for the pre-operative planning and the intra-operative replanning approach showed to be very robust to correct the initial predictions, showing average error smaller than 1 mm.