Sanitary professionals often experience burnout due to long working hours and the stress associated with handling sensitive situations. Consequently, in recent years, significant efforts have been made to automate various tasks within their roles using deep learning methods. This study proposes an automated system for generating summaries of medical consultations, aiming to alleviate doctors’ workloads. The architecture takes audio recordings of medical consultations as input, which are then transcribed and diarized for further processing. Subsequently, the entire transcription is summarized using state-of-the-art transformer-based models. For this study, a dataset comprising fourteen consultations, along with two summaries provided by different sanitary professionals, was utilized. In order to address the limitations of the available data, a data augmentation technique was implemented which makes use of the capabilities of large language models. The proposed models achieved a result of 0.52 in the ROGUE-1 metric, demonstrating the effectiveness of the proposed methodology.