Word sense disambiguation is an NLP task embedded in different applications. We propose to evaluate its contribution to the automatic translation of French texts into pictographs, in the context of communication between doctors and patients with an intellectual disability. Different general and/or medical language models (Word2Vec, fastText, CamemBERT, FlauBERT, DrBERT, and CamemBERT-bio) are tested in order to choose semantically correct pictographs leveraging the synsets in the French WordNets (WOLF and WoNeF). The results of our automatic evaluations show that our method based on Word2Vec and fastText significantly improves the precision of medical translations into pictographs. We also present an evaluation corpus adapted to this task.