Improving healthcare through technological innovation takes on a new dimension with the collaboration between Kereval and the Clinical Data Center (CDC) at Rennes University Hospital, who have joined forces with the Inserm DOMASIA research team from the Signal and Image Processing Laboratory. This collaboration, aligned with the objectives of the ONCOFair project, is dedicated to optimizing the quality of information within healthcare data warehouses.

Led by Professor Marc Cuggia, the initiative focuses on advancing the exploitation of healthcare data. The development of innovative methods integrating artificial intelligence and automatic natural language processing is at the heart of this collaboration, with the intention of enriching the quality of available healthcare data.

Medical data management is an essential pillar for innovation and improved patient care. Whether this data comes from administrative records, clinical reports, biological analyses or imaging, its structured integration in a secure environment is imperative for efficient reuse.

Medical data management is an essential pillar for innovation and improved patient care. Whether this data comes from administrative records, clinical reports, biological analyses or imaging, its structured integration in a secure environment is imperative for efficient reuse.

Health Data Warehouses (HDWs) play a key role in this strategy, centralizing data from various medical information systems in a consistent format, facilitating their use for research or patient management. Faced with the rapid expansion of EDS in France, collaboration is focusing on improving data quality, which remains a major challenge.

The challenge is to limit the risk of errors in data processing and the development of AI algorithms. Indeed, AI performance depends on high-quality data. It is recognized that artificial intelligence can also be a vector for increasing this quality. By employing state-of-the-art pre-processing methods, the aim is to achieve more accurate and exhaustive data, enabling the development of reliable, high-performance decision-support tools for the benefit of patients.