Usability Study of CARTIER-IA: A Platform for Medical Data and Imaging Management

Artificial Intelligence algorithms’ application to medical data has gained relevance due to its powerful benefits among different research tasks. Nevertheless, medical data is heterogeneous and diverse, and these algorithms need technological support to tackle these data management challenges. The CARTIER-IA platform enables different roles (including principal researchers, IA developers and data collectors) to unify medical data, both structured data and DICOM images, and to apply Artificial Intelligence algorithms to them in a straightforward way through an online web application. However, given the diversity of roles involved in the platform, it is essential to account for its usability. It is necessary that users feel comfortable using the platform as relevant and complex tasks are carried out through its different services (such as the application of algorithms to the stored data, the manual edition of medical images or the visualization of structured data). This work presents a heuristic evaluation of the CARTIER-IA platform to improve its interaction mechanisms and get the most out of its functionalities.