Luis Martí-Bonmatí -Chairman of the Radiology Department at La Fe University and Technical Hospital and Director of the Biomedical Imaging research group- stands at the forefront of integrating Artificial Intelligence (AI) in medical imaging. His role extends to coordinating a significant network of EU research projects, and notably EUCAIM, the flagship European Cancer Imaging Iniative. This effort focuses on leveraging AI in cancer diagnosis and treatment, highlighting the transformative potential of AI in medical sciences.
Luis’ work in EUCAIM includes spearheading the creation of a research infrastructure designed to facilitate the application of AI in health imaging. This initiative aims to collect cancer images, which will be made accessible to researchers, enabling advancements in image harmonization, standardization, normalization and resizing using AI technologies. “Next year, we will be able to have more than 60 million cancer images that will be accessible to allow researchers to perform AI developments” he explains. EUCAIM has garnered the support of approximately 76 partners, all united in their commitment to advancing medical imaging and the quantitative extraction of information from images for AI development.
One of the critical challenges Luis and his team face is the barrier to data access, a significant hurdle in medical imaging research. To overcome this, they advocate for federated developments and play an instrumental role in defining standards, architectures and ontologies in the field. These efforts aim to streamline research processes and enhance the utility and interoperability of medical imaging data across various platforms.
His vision also extends beyond the technical aspects of AI in medical imaging: Luis is keen on improving the precision and reliability of disease diagnosis and treatment, with a focus on cancer. By leveraging AI, he seeks to enhance the interpretation of medical images, allowing for more accurate assessments of tumor states and grades. “We are really willing to push AI to be released as a framework with demarcation parameters not related to the vendor or the protocol but to the disease expression”. This patient-centric approach prioritizes the disease’s characteristics over the technological specifics of imaging equipment, aiming for a more accurate and meaningful analysis of patient data.
The ultimate goal is to link existing repositories for research and utilize federated APIs to facilitate seamless access to data, thereby enhancing research capabilities and outcomes. These initiatives represent a significant leap forward in the application of AI in healthcare, specifically in the realm of cancer research. By addressing the challenges of data access and standardization, Luis and his collaborators are setting the stage for groundbreaking advancements in medical imaging and the broader application of AI in health diagnostics and treatment planning.