Language Technology is an essential component of many Cyber-Physical Systems (CPSs) because specialized linguistic knowledge is indispensable to prevent fatal errors. We present the case of automatic identification of implant terms. The need of an automatic identification of implant terms spurs from safety reasons because patients who have an implant may or may be not submitted to Magnetic Resonance Imaging (MRI). Normally, MRI scans are safe. However, in some cases an MRI scan may not be recommended. It is important to know if a patient has an implant, because MRI scanning is incompatible with some implants. At present, the process of ascertain whether a patient could be at risk is lengthy, manual, and based on the specialized knowledge of medical staff. We argue that this process can be sped up, streamlined and become safer by sieving through patients' medical records. In this paper, we explore how to discover implant terms in electronic medical records (EMRs) written in Swedish with an unsupervised approach. To this aim we use BERT, a state-of-the-art deep learning algorithm based on pre-trained word embeddings. We observe that BERT discovers a solid proportion of terms that are indicative of implants.