The increasing amount and availability of collected data (cancer imaging) and the development of novel technological tools based on Artificial Intelligence (AI) and Machine Learning (ML), provide unprecedented opportunities for better cancer detection and classification, image optimization, radiation reduction, and clinical workflow enhancement. The INCISIVE project aims to address three major open challenges in order to explore the full potential of AI solutions in cancer imaging: (1) AI challenges unique to medical imaging, (2) Image labelling and annotation and (3) Data availability and sharing. In order to do that INCISIVE plans to develop and validate: (1) an AI-based toolbox that enhances the accuracy, specificity, sensitivity, interpretability and cost-effectiveness of existing cancer imaging methods, (2) an automated-ML based annotation mechanism to rapidly produce training data for machine learning research and (3) a pan-European repository federated repository of medical images, that will enable the secure donation and sharing of data in compliance with ethical, legal and privacy demands, increasing accessibility to datasets and enabling experimentation of AI-based solutions. The INCISIVE models and analytics will utilize various cancer imaging scans, biological data and EHRs, and will be trained with 1 PB of available data provided by 8 partners within the project. INCISIVE solution will be investigated in four validation studies for Breast, Prostate, Colorectal and Lung Cancer, taking place in 8 pilot sites, from 5 countries (Cyprus, Greece, Italy, Serbia and Spain), with participation of at least 2,600 patients and a total duration of 1.5 year. INCISIVE moves beyond the state of the art, by improving sensitivity and specificity of lower cost scanning methods, accurately predicting the tumor spread, evolution and relapse, enhancing interpretability of results and “democratizing” imaging data.

SENSOPAD initiative heralds a transformative era in women's health by addressing the longstanding challenges posed by Endometriosis (ED). ED's early detection is often underdiagnosed due to its asymptomatic nature, pivotal for improved health outcomes and reduced healthcare costs. SENSOPAD introduces two pioneering ED sensing systems: sensoPAD, seamlessly integrated into sanitary pads, and sensoMFgFET, a portable Point-of-Care (POC) device. SensoPAD comprises three Biological Processing Units (BPU) - BPU-1, BPU-2, and BPU-3 - ensuring continuous monitoring. BPU-1 combines an electrochemical sensor with RFID, while BPU-2 merges a chemical-based fuel cell with an electrochromic cell. BPU-3, an energetically autonomous unit, synergizes BPU-1 and BPU-2 capabilities for seamless monitoring. SensoMFgFET, a POC device, utilizes microfluidic systems and bio-conjugated gFET with RFID functionality, capturing specific DNA SNPs from the menstrual fluid as early ED indicators. Both devices are complemented by a user-friendly mobile application enabling real-time data acquisition and analysis. An advanced cloud platform integrated with AI enhances diagnostic accuracy. This innovative approach shifts the paradigm in ED detection, empowering women, clinicians, and healthcare systems. SensoPAD detects biomarkers, providing insights during menstrual cycles. If concerns arise during its use, users transition to sensoMFgFET at clinical points, combining at-home convenience with clinical precision. SENSOPAD aims to reduce ED diagnosis time from eight years to days, enabling early treatment, preventing symptom deterioration, optimizing infertility care, and streamlining healthcare journeys. Integrating SENSOPADs with a mobile app and AI-enriched cloud platform ensures accurate, cost-effective diagnostics, detecting silent instances of ED, and signifies a paradigm shift, fostering informed, proactive, and inclusive healthcare decisions for women worldwide.

EQUICARES supports access to innovative and sustainable mental health and care services by people in vulnerable situations through a blend of research, co-creation and policy solutions. The project uses innovative methodologies, such as an advanced application of Levesque framework for evaluation of mental health services; the deployment of Computational Social Sciences to increase access for hard-to-reach populations in vulnerable situations and collect accurate quantitative and qualitative data on inequalities in mental healthcare services; and the combination of complementary cost-analysis techniques. To unlock the design of innovative solutions, the project maps, through digital ethnography, existing innovative solutions, analyses for accessible mental health services and links them with different parts of the mental health system. Such informative insights are visualised and offered to policy makers through a dedicated Atlas. EQUICARES pilots innovative solutions in 8 areas from 7 countries, which represent diverse socioeconomic settings and cover all major categories of vulnerable groups, while informing various strategic frameworks of EU. Through “Smart Health Labs”, the project engages vulnerable groups at the community level to co-design, implement and assess innovative solutions based on the principles of social economy and user innovation. At the individual level, the project provides awareness raising and capacity-building and pilots a novel AI-based Assistant, making advancements in the landscape of AI-generated mental health ecosystem, and fostering mental health and digital literacy of users. EQUICARES tests the value of its innovative solutions and applies novel cost analysis techniques to provide solid evidence on the negative impact on not taking measures. Finally,the project replicates its outcomes in 4 additional cases and develops the Inclusive Mental Health and Care Policy Dashboard towards the sustainability and policy uptake of its results.

Competitive conflicts for land use between the energy and food sectors have appeared, which could be mitigated by the vertical integration of RES in farms through new circular business models. By this approach, farms will become climate neutral, optimising their production and reducing their impact on natural resources and biodiversity, on top of providing energy services to communities and diversifying their economic income. However, there is a need to identify, understand and overcome major existing barriers perceived by agricultural communities. Moreover, current initiatives do not to effectively consider and address the complex interactions and factors from the farming and RES context, thus missing to support decision making based on accurate projections, estimations and forecasts. HarvRESt will work on these needs by improving the existing knowledge and its fragmented status, which will be feed to an Agricultural Virtual Power Plant able to run different scenarios and farm configurations to determine the best operation procedures for a given RES solution. This data will be then provided to a decision support system able to weight trade-offs and key indicators to provide ad-hoc recommendations to farmers and policy makers, thus enabling the consecution of improved production rates on renewable energy, food & feed within agro communities. For the successful execution of HarvRESt and implementation of recommendations, a multi-actor approach fostering co-creation sessions together with the provision of training materials for farmers empowerment will be implemented. The full approach of HarvRESt will be supported and executed at 4 use cases representing different topologies of farms, a diversity of stakeholders and organizational structures, distinct geographical conditions and a wide variety of RES technologies. Together with HarvRESt community and mapped initiatives, the project will act as a hub for knowledge and best-practices on RES integration at farm level.

InnoRate is set on deploying a trusted, objective and recognised service platform across the EU and AC to support and improve the decision-making processes of investors and lenders for vetting, prioritising and providing access to finance to innovative high growth potential SMEs. The suite of digitally-enabled decision support tools and services provided through our platform are underpinned by well-customised innovation assessment and rating methodologies that go beyond the unsuitable, backward-looking and costly practices currently employed, so as to evaluate and signal the technological and business potential and risks of market-creating innovations, efficiently and cost-effectively. To this end, we leverage semantic technology, existing data sources and in-depth human expertise, to minimise the time and resources (human and financial) required by investors and lenders in this respect, reducing knowledge / information asymmetries and ultimately risk premiums paid by innovative project managers. In doing so, our ambition is clearly to disrupt the largely risk-averse financial sector of the EU and AC, enhancing the innovation capacity of high growth technology sectors and SMEs, paving the way for drastical innovations to flourish and placing Europe in the forefront of the global innovation game. To put it simply, we want to bring innovation to finance and finance to innovation.