Robotic patients show great potential for helping to improve medical palpation training, as they can provide feedback that cannot be obtained in a real patient. They provide information about internal organ deformation that can significantly enhance palpation training by giving medical trainees visual insight based on the pressure they apply for palpation. This can be achieved by using computational models of abdomen mechanics. However, such models are computationally expensive, and thus unable to provide real-time predictions. In this video, researchers proposed an innovative surrogate model of abdomen mechanics by using machine learning (ML) and finite element (FE) modelling to virtually render internal tissue deformation in real time. Researchers first developed a new high-fidelity FE model of the abdomen mechanics from computerized tomography (CT) images. Researchers performed palpation simulations to produce a large database of stress distribution on the liver edge, an area of interest in most examinations. Researchers then used artificial neural networks (ANNs) to develop the surrogate model and demonstrated its application in an experimental palpation platform. The FE simulations took 1.5 h to predict stress distribution for each palpation while this only took a fraction of a second for the surrogate model. The results show that their artificial neural network (ANN) surrogate has an accuracy of 92.6%. Researchers also showed that the surrogate model is able to use the experimental input of palpation location and force to provide real-time projections onto the robotics platform. This enhanced robotics platform has the potential to be used as a training simulator for trainees to hone their palpation skills.