Abstract
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.
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Acknowledgements
This research work has been supported by the Spanish Ministry of Education and Vocational Training under an FPU fellowship (FPU17/03276). This work was also supported by national (PI14/00695, PIE14/00066, PI17/00145, DTS19/00098, PI19/00658, PI19/00656 Institute of Health Carlos III, Spanish Ministry of Economy and Competitiveness and co-funded by ERDF/ESF, “Investing in your future”) and community (GRS 2033/A/19, GRS 2030/A/19, GRS 2031/A/19, GRS 2032/A/19, SACYL, Junta Castilla y León) competitive grants.
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Vázquez-Ingelmo, A. et al. (2021). Usability Study of CARTIER-IA: A Platform for Medical Data and Imaging Management. In: Zaphiris, P., Ioannou, A. (eds) Learning and Collaboration Technologies: New Challenges and Learning Experiences. HCII 2021. Lecture Notes in Computer Science(), vol 12784. Springer, Cham. https://doi.org/10.1007/978-3-030-77889-7_26
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