Robotics is a key technology to improve and support the medical services by increasing efficiency and improving quality of care. One particular challenge is medical palpation, where a physicians uses their hands to manually feel and identify abnormalities or tumors. This is a challenging procedure that takes many years to learn. A soft robotic phantom which could provide feedback on palpation technique could enable medical professionals to learn more easily. Furthermore, this would allow quantitative information about how practitioners perform the technique to be gained. This can be used to inform and move towards robotic palpation that mirrors the human performance. In this work, we demonstrate an approach for developing a large-area sensorized phantom that uses sensor morphology to sense over a large areas. A reconstruction algorithm allows the depth of palpation to be measured and the location to be identified with a precision of ± 5mm. This phantom was used to measure deformation from human palpation, with the resultant information used to replicate the behaviour with a robot hand. To the authors' knowledge this is the first sensorized phantom that characterizes large scale deformation from human palpation to inform the performance of the procedure by a robot. © 2020 IEEE.