Discover HaDEA-managed AI-related projects

The European Union's approach to artificial intelligence (AI) is rooted in fostering excellence and trust, with a focus on upholding safety and fundamental rights while enhancing research and industrial capacity.  

The European AI Strategy aims to position Europe as a leading hub for human-centric and reliable AI, supported by a comprehensive framework introduced in April 2021, which includes a regulatory framework, a coordinated plan with member states, and a communication on European AI approaches.  

To reinforce this strategy, in January 2024, the Commission unveiled an AI innovation package to aid startups and SMEs in creating AI that aligns with EU standards. A pivotal part of this package is the strategic investment framework encapsulated by the "GenAI4EU" initiative, designed to accelerate generative AI integration within the Union's strategic industries. To achieve global competitiveness, the EU is committed to transforming AI from research to market, promoting societal benefits, and attaining strategic leadership in key sectors.  

This ambition is underpinned by substantial investments, including €1 billion annually from Horizon Europe and Digital Europe programmes, additional private and state funding aiming for €20 billion yearly, and €134 billion allocated for digital advancements through the Recovery and Resilience Facility. The EU's approach also emphasizes the importance of high-quality data, supported by initiatives such as the EU Cybersecurity Strategy, Data Act, and Data Governance Act, to develop robust and high-performance AI systems. 

AI in HADEA-managed projects

HaDEA is actively contributing to this AI revolution by funding AI-related projects. Today, HaDEA manages more than 900 projects that either develop or apply AI tools. These projects range widely, from applying AI to improve healthcare diagnostics, to boosting manufacturing efficiency, enhancing earth observation capabilities, and refining algorithms for the Internet of Things.  

Here are some examples: 

Healthcare 

HaDEA-managed projects are innovating and reimagining healthcare, from the diagnosis to the storage of your data. An example is PRIMAGE, an open cloud-based platform aimed at supporting decision making in the clinical management of paediatric cancers. 

AI and Big Data are opening new avenues for improving healthcare. However, they also present risks for the security of sensitive clinical data. FeatureCloud addresses these risks with a transformative security-by-design concept without sharing sensitive data via any communication channels and no data storage at a central point. 

Furthermore, BRAINTEASER harnesses AI to predict disease progression, enable early detection, and prevent complications in amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS). By integrating clinical data with patient-generated information, the project advances personalised medicine, equipping patients and clinicians with actionable insights. 

Similarly, PANCAIM leverages AI to merge genomics and imaging data, deepening the understanding of pancreatic ductal adenocarcinoma (PDAC) and refining patient stratification. By developing AI models for early detection and personalised treatment, the project aims to enhance patient outcomes and revolutionise pancreatic cancer care. Another area where AI is making a significant impact is in biomedical imaging. The HADEA-managed projects uCAIR and PHOREVER are developing innovative technologies to enhance the way cells are examined by scientists and healthcare professionals. uCAIR is creating a versatile, practical multimodal photonics platform and combines non-linear optical fibres, optronics, wide-band coherent Raman and artificial intelligence technologies to image at video rates and track cells and biological tissues with augmented chemometric digitalisation. PHOREVER is creating a multi-sensing platform that utilises photonic integrated circuits in TriPleX technology for the detection of extracellular vesicles with size down to 80 nm and plans to use AI-powered data analysis to correlate the measurement data to disease-specific medical information. 

Lastly, The FLUTE project aims at advancing AI-driven federated learning methods and enhancing privacy-preserving cross-border data sharing in medical research. The project also enables cost-effective AI training in decentralised environments, reducing dependency on large, centralised datasets. Furthermore, it improves early diagnostics and AI-driven medical imaging leading to lower healthcare expenses and faster clinical decision-making. 

AI development 

Some of our projects are paving the way to innovation and development in the AI research. AI-SPRINT aims to democratise access to AI technologies, by simplifying and accelerating the development of AI applications through edge computing. The tools developed during the project were validated by three case studies in personalised healthcare, agriculture 4.0, and maintenance and inspection. 

Human-Robot Collaboration 

HaDEA-manages a number of other projects in the area of human-robot collaboration, developing tools applicable across various sectors. 

SOPRANO is coalescing multidisciplinary research and innovation in human-robot collaboration and intelligent multi-agent systems to design the next generation of manufacturing floors, construction sites, and agri-food production, where humans and intelligent machines will seamlessly work together. The project proposes to scale collaboration from the single human-agent dyad to a peer-based synergy between multiple interconnected robotic systems, supporting various tasks in collaboration with human workers, robotics, and other agents. 

The MAGICIAN project develops modular automation solutions for defect detection and rework in manufacturing, addressing consumer expectations for aesthetic quality while ensuring safer working conditions. Two robotic solutions, driven by AI modules, will detect defects and decide on rework strategies using multi-modal data and machine learning algorithms. With a human-centred approach, the project integrates robotics into production processes, enhancing safety and quality, and paving the way for a new era of impeccable production. 

The SMARTBEAR consortium has successfully deployed the foundational infrastructure and e-services, designed to manage and analyse data from sensors and medical records. The SB Architecture has been restructured to meet the needs of users and facilitate machine-to-machine collaborations with other EU projects. The core components and tools have been developed and evaluated through a pilot initiative, which enrolled over 100 participants and established synergies with other EU projects. The application of artificial intelligence algorithms to data sets enables the generation of individualised statistical inferences, allowing for the correlation of clinical evidence with machine learning results for complex patient profiles. This enhances the standard of care delivered, providing a personalised approach to patient management and treatment.

The ALAMEDA project successfully developed the ALAMEDA AI Toolkit and implemented the ALAMEDA Innovation Hub Prototype, showcasing advancements in the early diagnosis and treatment of brain diseases through AI and technological interventions. ALAMEDA is dedicated to advancing the understanding of brain disease progression, harnessing the power of Artificial Intelligence and digital tools. In three years of intensive work ALAMEDA aimed at enhancing outcome predictability and the management of brain diseases. ALAMEDA exploited the use of digital technologies and the clinical experts within the consortium to generate rich diagnostic data streams, while also looking at the side of patients and caregivers in terms of acceptability and usage in their daily lives. 

The CLARIFY project aims to improve the quality of life for cancer survivors, particularly those with breast, lung, and lymphoma cancers. Using Big Data and artificial intelligence (AI) techniques, the project identified factors that predict poor health status after cancer treatment. It also developed a digital platform (Clarify Platform) that integrates patient information from various sources, enabling real-time analysis and characterisation of patient cohorts. Moreover, it created predictive models to identify patients at risk of relapse or toxicity, using statistical relational learning and explainable AI techniques.  By achieving its goals, the project aims to address the challenge of improving the post-treatment quality of life for cancer survivors, who now make up over 50% of adult patients diagnosed with cancer in the US and Europe. 

Chronic respiratory diseases are non-communicable diseases with unknown molecular mechanisms. Respiratory syncytial virus (RSV) is a major risk factor for asthma development. RSV infects nearly all infants before the age of 2 years and contributes to asthma. The CLARITY project aims to identify genetic risk factors and mechanisms underlying virus-induced asthma. It will use two national cohorts to identify human genetic risk factors and RSV strains. Artificial Intelligence (AI) will be used to integrate data and identify drug-like compounds. The project will validate mechanisms and compounds in patient-derived models and human trials. CLARITY will impact the understanding, prevention, and treatment of virus-triggered asthma. The results will enable development of genetic risk scores and personalised prevention campaigns. The project's findings will also contribute to the development of mechanism-targeted drugs. 

The CHAIMELEON project aims to improve cancer management in Europe by developing a repository of health images and clinical data for the most prevalent cancers (lung, breast, prostate, and colorectal). The project has created a structured repository with anonymised data, accessible via a web interface and it has validated the repository with AI developers, clinicians, and legal experts. Moreover, it has opened the repository for external validation, inviting the global AI community to test and train their models. The expected impact of the project includes improving AI health imaging standards, providing AI-based solutions for diagnosis, treatment, and follow-up, and increasing trust in AI solutions. Overall, the project aims to address the lack of data availability and improve cancer treatment procedures by developing and validating AI tools, ultimately leading to better cancer management and outcomes. 

The aim of the PRAESIIDIUM project is to develop a prototype tool for the real-time prediction of the prediabetic risk based on a series of patient-specific mathematical models that simulate metabolism, pancreas hormone production, microbiome metabolites, inflammatory process, and immune system response. The prediction algorithm will be based on a “physics-informed machine learning.” Datasets of real-life data will be combined with mathematical models to overcome the limits of a “black-box” machine-learning approach, while reducing the computational time for simulating the solutions of heavy mathematical models and improving its prediction performances. The project succeeded in developing a Physics-Informed Machine Learning. 

Industry and Manufacturing 

AI is being used by some HaDEA-managed projects to enhance the manufacturing process, aiming to create a green, circular, and sustainable process. The DaCapo project is developing a comprehensive, human-centric AI framework with digital tools and services to promote Circular Economy (CE) adoption in EU manufacturing. By reducing reliance on imported materials and improving raw material efficiency, the project aims to significantly cut production costs, benefiting the European economy. 

Other projects also focus on zero-defect manufacturing, aiming to reduce waste, optimise energy use, and produce higher-quality products with minimal environmental impact. By leveraging digital tools like AI, IoT devices, and smart sensors, companies can boost competitiveness and sustainability. Projects like LEVEL-UP and RECLAIM extend machinery lifetimes, while DAT4ZERO and OPTIMAI focus on predictive maintenance to enhance efficiency. InterQ project and the i4Q use AI to support zero-defect manufacturing by analysing production data, all contributing to greener and more efficient manufacturing. 

Additionally, SoliDAIR aims to accelerate the uptake of AI and Robotics in European manufacturing, using Data as an enabler. The project will co-develop and demonstrate tailored solutions to digitalise and automate visual inspection and physical testing, enable predictive quality control and process optimisation. By leveraging the current state of the art in visual AI, AI for process data, and smart & collaborative Robotics, SoliDAIR will improve production processes through digitalised and automated quality control for high volume, high rate and flexible manufacturing. The project's objective is to increase process efficiency and flexibility, while improving the working conditions of the process staff, and ultimately facilitate a strong increase in the competitiveness and sustainability of the EU manufacturing companies across sectors. 

Space research 

AI is being leveraged in several HaDEA-managed projects in the field of space research and Earth observation. These projects are driving innovation in space technology. 

Evoland is developing eleven innovative prototypes for the Copernicus Land Monitoring Service (CLMS), using advanced biomass mapping, data fusion, and Machine Learning to continuously monitor land surface dynamics, biomass, and status. By harnessing the power of Weakly Supervised Learning (WSL), the project will reduce Copernicus Sentinel 1 and 2 data size, making it more accessible to users. 

In ENLIGHTEN and ENLIGHTEN-ED, AI and machine learning power a Health Monitoring System for engine testing and future flights. Algorithms for decision making are designed using AI tools/software to detect failure and provide a diagnostic, using past flight data for system training. 

Data Analysis and Cognitive Computing Continuum 
 
HaDEA is also promoting projects in the field of edge computing and cognitive systems, with multiple applications and benefits. 

For instance, ENACT is developing a Cognitive Computing Continuum that can address the needs for optimal resource management and dynamic scaling. By harnessing the power of AI-powered Graph Neural Networks and Deep Reinforcement Learning agents, the project will suggest optimal deployment configurations for hyper-distributed applications. The outcomes of these research and development activities will enhance European businesses competencies and upgrade IT services. 

COGNIFOG will provide a software solution as a Cognitive Fog Framework (Cognitive-Fog) for monitoring and analysing data flows along the IoT-edge-cloud continuum.  The project utilises AI-based analytical services within its Cognitive-Fog environment, enabling the processing of data closer to its source and providing real-time responses for many smart IoT applications for adaptability, dependability, scalability, and energy efficiency.