Programpunkter

Equal access to health data is far from reality today and there is a lack of comparison between countries. To motivate, understand and increase progress, we start from the position that highlighting current status, sharing evidence related to practices and policies, and identifying major enablers and obstacles across national and regional boundaries is necessary. Therefore, we have invited panel participants to introduce five different countries, representing five different stages of and approaches to patients’ ORA implementation.

How do you go from being a novice to becoming skilled in biomedical and health informatics, if you’re not on the list for comprehensive and formal education within the field? We studied this and will present our insights.

More than 40% of the adult population suffers from functional gastrointestinal disorders, now considered disorders of the “gut-brain axis” (GBA) interactions, a very complex bidirectional neural, endocrine, immune, and humoral communication system modulated by the microbiota. To help discover, understand, and manage GBA disorders, the OnePlanet research center is developing digital twins focused on the GBA, combining novel sensors with artificial intelligence algorithms providing descriptive, diagnostic, predictive or prescriptive feed-back.

The aim of this presentation and paper is to identify the underlying obstacles and provide recommendations to promote the dissemination of competency-based learning objectives catalogs for curricular development in health data and information sciences.

Deep learning models for radiology are typically deployed either through cloud-based platforms, through on-premises infrastructures, or though heavyweight viewers. This tends to restrict the audience of deep learning models to radiologists working in state-of-the-art hospitals, which raises concerns about the democratization of deep learning for medical imaging, most notably in the context of research and education. We show that complex deep learning models can be applied directly inside Web browsers, without resorting to any external computation infrastructure, thanks to the use of WebAssembly, and we release our code as free and open-source software. This opens the path to the use of teleradiology solutions as an effective way to distribute, teach, and evaluate deep learning architectures.