The Social Economy (SE) sector constitutes over 10% of all EU enterprises, sustaining around 13.6 million job positions. Despite its significant role, Social Economy Organizations (SEOs) face substantial policy and legal challenges, including funding shortfalls and difficulties in scaling, underscoring the need for thorough investigation. While often acknowledged as innovative contributors to social inclusion, some criticize them for allegedly advancing a neoliberal agenda. Controversies persist, primarily due to challenges in measuring the social impact of SEOs and discerning their outcomes compared to mainstream social inclusion providers. To delve into this discussion, there is a pressing need to enhance evaluation tools for measuring the impact of SE on social inclusion. Although various approaches exist, they are primarily linked to improved accountability and external evaluation guidelines, commonly overlooking local needs and specificities. Concurrently, transnational reports indicate that SE employees, on average, receive lower compensation, experience more precarious jobs, and face informal working conditions. Therefore, there is an urgent need to broaden the scope of social impact assessment beyond service beneficiaries, incorporating the impact on SE employees. In this context, technological advancements hold the potential to support SE progression, with platform cooperatives emerging as influential contributors committed to citizen involvement and improving members' working conditions through collaborative governance. Against these backdrops, ASSETS employs a four-step strategy to explore SE's impact on social inclusion, quality job provision, and sustainability: conducting research and case studies spanning regions both within and outside the European Union, improving evaluation tools through local integration and technological advancements, and creating a digital collaborative platform to engage stakeholders and enhance working conditions within SE.

HIVE adopts an Implementation Research Framework to support integrated care and management of HIV and concomitant NCDs leveraging an mHealth application tailored for the (self-)management of multiple long-term conditions for PLWH. This initiative also incorporates interpersonal counselling (IPC) for vital psychosocial support and integration of NCDs care into HIV clinics. The mHealth application will build on an existing digital health tool developed by HIVE partner, Columbia University, which has already been piloted in several low- and middle-income countries . HIVE aims to extend and customize the application for diverse high-income countries (HICs) and low- and middle-income countries (LMICs), including Kenya, Kazakhstan, Greece and Malta, addressing the unique needs of PLWH in different disease settings and cultural contexts. The goal is to mitigate the disparities in access to health for PLWH in different countries as well as the inequalities that are being faced among different population groups within each participating country.

Psoriatic Arthritis (PsA) is a chronic, progressive, inflammatory disease affecting 1-2% of the general population, while manifesting in up to 30% of people with psoriasis (PsO). The transition from health to PsA is currently untraceable; diagnosis of early PsA is challenging even in PsO patients. Untimely diagnosis is common and contributes to early deterioration of quality of life, also increasing the burden of the multiple comorbidities associated with PsA. In this vein, iPROLEPSIS aspires to shed light upon the health-to-PsA transition with a comprehensive multiscale/multifactorial PsA model employing novel trustworthy AI-based analysis of multisource and heterogenous (i.a., in-depth health, environmental, genetic, behavioural) data, digital phenotyping of inflammatory symptoms with emphasis on tracking of motor manifestations using smart devices and wearables, novel optoacoustic imaging-based markers of PsA in the skin and joints, and investigation of the role of mast cells in the PsA transition, to identify key drivers of the disease and support personalized models for PsA risk/progression prediction and monitoring as well as associated inflammation detection and severity assessment. To ultimately advance PsA diagnosis and care, the models will be translated into a digital health ecosystem comprising dependable tools for supporting healthcare professionals in disease screening, monitoring and treatment via quantitative, explainable evidence, and empowering people with/at risk of PsA with tailored insights and preventive interventions based on actionable factors for educated health management. The project will steer its research and development efforts following a trustworthy framework for ethical, lawful, and robust AI, and a user-centered co-creation approach based on constant involvement of key stakeholders during the design, development, and testing of the digital health ecosystem, securing successful integration of the latter in the continuum of care.

Parkinson’s disease (PD) is the most common neurodegenerative movement disorder, with a multifactorial aetiology, heterogeneous manifestation of motor and non-motor symptoms, and no cure. PD is often missed or misdiagnosed, as early symptoms are subtle and common with other diseases, allowing for considerable damage to occur before treatment. Moreover, selecting the optimal medication regimen is usually a lengthy, “trial and error” process, leading to critical, costly non-adherence. Following a trustworthy and inclusive approach to AI development and based on multidisciplinary expertise and broad stakeholder engagement, AI-PROGNOSIS aims to advance PD diagnosis and care by: 1) developing novel, predictive AI models for personalised PD risk assessment and prognosis (in terms of time to higher disability transition and response to medication) based on multi-source patient records and databases, including in-depth health, phenotypic and genetic data, 2) implementing a system of biomarkers informing the AI models by tracking key risk/progression markers in daily living, and ultimately 3) translating the models and digital biomarkers into a validated, privacy-aware AI-driven toolkit, supporting healthcare professionals (HCPs) in disease screening, monitoring and treatment optimization via quantitative, explainable evidence, and empowering individuals with/without PD with tailored insights for informed health management.

Artificial intelligence (AI) enables data-driven innovations in health care. AI systems, which process vast amounts of data quickly and in detail, show promise both as a tool for preventive health care and clinical decision-making. However, the distributed storage and limited access to health data form a barrier to innovation, as developing trustworthy AI systems requires large datasets for training and validation. Furthermore, the availability of anonymous datasets would increase the adoption of AI-powered tools by supporting health technology assessments and education. Secure, privacy compliant data utilization is key for unlocking the full potential of AI and data analytics. In this proposal, we will advance the current state-of-the-art data synthesis methods towards a more generalized approach of synthetic data generation. We will also develop metrics for testing and validation, as well as protocols that enable synthetic data generation without access to real-world data (through multi-party computation). We aim to provide: 1) Improved methods and technical pipelines for privacy-preserving data synthesis including different data formats such as EHRs and medical images, 2) Easy to use and configurable data services to enable AI developers’ access to larger pools of decentralized de-identified data through multi-party computing, 3) Provide anonymous data on demand or from a (temporary) repository, 4) Establish a Data Market – facilitating data sharing and monetization incl. incentives-based provision of data to the services, 5) Integrate the data market and the data service ecosystem as a X-European health data hub in the European Health Data Space, and 6) Validate the results with real-world use-cases focusing on high impact diseases, cancer types in particular.