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.

Separate registration required: https://www.mie2023.org/tutorials,With this tutorial, participants will gain a detailed insight into the Data Sharing Framework (DSF). The open source DSF enables users to execute biomedical research and healthcare delivery processes across organizations, and the tutorial will demonstrate this with examples from the German Medical Informatics Initiative (MII) funded by the Federal Ministry of Education and Research (BMBF). The tutorial will cover fundamental concepts of distributed processes, the DSFs architecture and key standards such as HL7 FHIR and BPMN 2.0. Participants will have the opportunity to gain hands-on experience with the DSF by working with different processes in a lab setting. Technical aspects such as authentication and authorization will be covered, as well as guidance on using the DSF for other use cases. This tutorial is designed for those involved in distributed research projects, including project members and software developers, as well as individuals interested in multi-organizational research projects.Additional information: https://dsf.dev/tutorials/MIE2023.html

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.