AYAs face a distinct set of challenges, including increased risks of secondary cancers, cardiovascular diseases, infertility, and mental health issues like anxiety and depression. Oncology programs delivering care to AYAs provide survivors with individualised survivorship care plans at the end of treatment, to accompany follow-up with their primary care providers. However, adherence to these plans is often scarce, due to differences in AYA survivors' age, sex, cancer treatment received, and socioeconomical conditions. Knowledge and understanding of such factors and of related late effects and impacts on quality of life is fundamental to providing effective survivorship care. The ubiquitous use of mobile devices by AYAs offers an opportunity to increase knowledge and understanding of AYAs cancer survivors’ health status and behaviours, allowing the modelling of late effects and quality of life trajectories in the different age subgroups. Digital health interventions overcome many barriers to AYA participation in survivorship programs. LATE-AYA seeks to fill this gap by empowering AYAs to better manage their health and well-being post-cancer, by developing an AI-driven digital phenotyping platform for managing late effects (LE) of cancer treatment. The project will implement a holistic, non-invasive approach through digital tools such as smartphones and wearables to monitor physical, psychological, and social well-being. The project will focus on preventive health behaviours, psychological support, and social reintegration, providing personalized care through digital interventions. LATE-AYA will contribute to long-term quality of life improvements, facilitate early detection of LE, and provide data-driven insights on the impact of lifestyle choices on health outcomes. This project is supported by a diverse consortium of European institutions and will leverage the UNCAN.eu platform to share data and models, fostering wider collaboration across the EU. This action is part of the Cancer Mission cluster of projects on “Quality of life (AYA)”.

AI4Deunk aims to develop 4 human-centered AI-powered interfaces: a web plug-in, a collaborative platform, a smartphone app, and an AR interface. All these solutions will be built upon a "debunking" API. To develop such an API, AI4Debunk will focus on 2 peculiar disinformation topics: the war in Ukraine and disinformation related to climate change. By deeply analysing these case studies thanks to sociological experts (targets groups, sources, way of diffusion...), we will map disinformation contents on knowledge graphs, and compare them to existing databases thanks to AI methods such as NLP. These knowledge graphs will be the skeleton of our technical work: one will be multimodal meaning that all types of modalities (written content, image, video) will be directly embedded, while the monomodal will be only about written content (the different modalities will be previously embedded into written content). Then debunking modules will be developed, for each type of modality, based on AI and ML methods that could detect and examine questionable content, by comparing it to the context extracted from the knowledge graphs. RNN, CNN, multi-modal language interpretation, and transformers methods will be used to that extent. The goal of such modules is to give a score of fakeness, which we will call Disinofscore, for content given as input. The API will then integrate this method and will be the bases of the 4 interfaces abovementioned. The plugin will allow people to be instantly noticed when a content they are considering is fake or true, the collaborative platform - also called Disinfopedia, will allow more proactive online users to report questionable content, that will be checked by a debunking committee chaired by a senior analyst, the app interface will allow people to check content in their everyday life, while the AR interface will allow citizens to have hindsight on the incoming generation of fake news and social media.