The public-private partnership, READI, seeks to help clinical studies (CS) to finally serve the complete general population, and therefore more patients. To date CS have struggled to recruit and retain participants from diverse backgrounds and communities, such as marginalized or disadvantaged groups (e.g., sexual, gender, age, cultural, and socioeconomic cohorts). The resulting knowledge gaps entrench or increase health disparities. The READI consortium strives to tackle these challenges by fostering a more cohesive and integrated CS ecosystem for underserved (US) and underrepresented (UR) communities. It will actively connect all key stakeholders who can facilitate access to a wide range of patient populations. It will provide these stakeholders with the necessary tools, training programs, and approaches essential for the recruitment and retention of US/UR patients in CS. In addition, it will design, build and implement a digital platform which is patient-centred, sustainable, open and innovative. This will foster improved access to CS information and READI tools, while also supporting patient connections with the created communities. Finally, at least 4 CS will be used for testing the effectiveness of the developed tools and approaches. READI has a three-fold objective: to help US/UR communities overcome CS participation barriers (e.g., lack of information/awareness, mistrust, poor communication, geographic limitations, prejudice), which in turn will improve research of many diseases and conditions, preventative care and treatment effectiveness in different demographic groups, and better serve society. READI’s success will draw from its interdisciplinary, multi-stakeholder, consortium composition of 73 organizations from 18 countries, with key expertise in drug development and CS (design and operations), engagement strategies for US/UR populations, digital platform development, training and capability building initiatives, effective communication and dissemination, long-term sustainability, ethics and regulatory affairs.

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)”.

In Europe, prostate cancer (PCa) is the second most frequent type of cancer in men and the third most lethal. Current clinical practices, often leading to overdiagnosis and overtreatment of indolent tumors, suffer from lack of precision calling for advanced AI models to go beyond SoA by deciphering non-intuitive, high-level medical image patterns and increase performance in discriminating indolent from aggressive disease, early predicting recurrence and detecting metastases or predicting effectiveness of therapies. To date efforts are fragmented, based on single–institution, size-limited and vendor-specific datasets while available PCa public datasets (e.g. US TCIA) are only few hundred cases making model generalizability impossible. The ProCAncer-I project brings together 20 partners, including PCa centers of reference, world leaders in AI and innovative SMEs, with recognized expertise in their respective domains, with the objective to design, develop and sustain a cloud based, secure European Image Infrastructure with tools and services for data handling. The platform hosts the largest collection of PCa multi-parametric (mp)MRI, anonymized image data worldwide (>17,000 cases), based on data donorship, in line with EU legislation (GDPR). Robust AI models are developed, based on novel ensemble learning methodologies, leading to vendor-specific and -neutral AI models for addressing 8 PCa clinical scenarios. To accelerate clinical translation of PCa AI models, we focus on improving the trust of the solutions with respect to fairness, safety, explainability and reproducibility. Metrics to monitor model performance and a causal explainability functionality are developed to further increase clinical trust and inform on possible failures and errors. A roadmap for AI models certification is defined, interacting with regulatory authorities, thus contributing to a European regulatory roadmap for validating the effectiveness of AI-based models for clinical decision making.