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PURPOSE: Electronic medical records are a valuable source of information about patients' clinical status but are often free-text documents that require laborious manual review to be exploited. Techniques from computer science have been investigated, but the literature has marginally focused on non-English language texts. We developed RUBY, a tool designed in collaboration with IBM—France to automatically structure clinical information from French medical records of patients with breast cancer. MATERIALS AND METHODS: RUBY, which exploits state-of-the-art Named Entity Recognition models combined with keyword extraction and postprocessing rules, was applied on clinical texts. We investigated the precision of RUBY in extracting the target information. RESULTS: RUBY has an average precision of 92.8% for the Surgery report, 92.7% for the Pathology report, 98.1% for the Biopsy report, and 81.8% for the Consultation report. CONCLUSION: These results show that the automatic approach has the potential to effectively extract clinical knowledge from an extensive set of electronic medical records, reducing the manual effort required and saving a significant amount of time. A deeper semantic analysis and further understanding of the context in the text, as well as training on a larger and more recent set of reports, including those containing highly variable entities and the use of ontologies, could further improve the results. RUBY extracts medical information from EMRs and populates clinical databases with high precision. [ABSTRACT FROM AUTHOR]