In the last years, the combined use of data-driven techniques and computational mechanics models has led to a plethora of techniques to be used as decision-support tools in biomedicine. Examples include image-based prediction of the fractional flow reserve (FFR) in cardiac patients [1], surgical planning of endovascular interventions or forecast of cancer progression via magnetic resonance imaging [2]. However, bringing these tools to clinical practice presents significant challenges, requiring efforts at multiple levels: basic research (modelling highly complex physics, developing precise and fast surrogates, ensuring robustness), applied research (modelling complex real-life scenarios, in lab validation, extensive exchange with clinicians) and final technology development and implementation with large scale clinical studies. This mini-symposium aims to present the latest advancements in any of the stages mentioned above. We invite talks on the following topics: - Development of image pre-processing techniques for computational modelling in biomedical applications. - Non-intrusive reduced order modelling and machine learning techniques for fast decision making in medicine. - Experimental and/or clinical validation of digital twins / data-driven decision support systems for biomedicine. - Use cases in medical devices, cardiovascular system, cancer treatment, traumatology, ophthalmology, respiratory system?, among others.