The rapid development and adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies have brought significant opportunities and challenges. While AI has the potential to revolutionise industries and improve lives, there are growing concerns related to privacy, security, fairness, transparency and the environmental footprint. The Olympics motto "Faster, Higher, Stronger" also applies to recent impressive AI advancements, but now is the time to update it to "Lighter, Clearer, Safer". We propose ACHILLES to build an efficient, compliant, and trustworthy AI ecosystem. At its core is an iterative development cycle inspired by clinical trials encompassing four modules. It begins with human-centric methodologies, followed by data-centric operations, model-centric strategies, and deployment-centric optimisations. It returns to human-centric approaches, focusing on explainability and model monitoring. This iterative cycle aims to enhance AI systems' performance, robustness and efficiency while ensuring they comply with the legal requirements and highest ethical standards. Another innovation is the development of an ML-driven Integrated Development Environment (IDE). The ACHILLES IDE will facilitate seamless integration between the iterative cycle's modules, enabling users to develop efficient, compliant, and trustworthy AI solutions more effectively and responsibly. The project aims to significantly impact European AI development, aligning with the region's guidelines and values. Through innovative techniques and methodologies based on the collaboration of a multidisciplinary team of 16 partners from 10 countries, ACHILLES will foster a strong AI ecosystem that respects privacy, security, and ethical principles across various sectors. By validating the results in real use cases (including healthcare, ID verification, content creation and pharmaceuticals), ACHILLES will showcase its practical applicability and potential for widespread adoption.

Background We propose a holistic view of interrelated frailties: cognitive decline, physical frailty, depression and anxiety, social isolation and poor sleep quality, which are a major burden to older adults and social and health care systems. Early detection and intervention are crucial in sustaining active and healthy ageing (AHA) and slowing or reversing further decline. Aims and Relevance The main aim of my-AHA is to reduce frailty risk by improving physical activity and cognitive function, psychological state, social resources, nutrition, sleep and overall well-being. It will empower older citizens to better manage their own health, resulting in healthcare cost savings. my-AHA will use state-of-the-art analytical concepts to provide new ways of health monitoring and disease prevention through individualized profiling and personalized recommendations, feedback and support. Approach An ICT-based platform will detect defined risks in the frailty domains early and accurately via non-stigmatising embedded sensors and data readily available in the daily living environment of older adults. When risk is detected, my-AHA will provide targeted ICT-based interventions with a scientific evidence base of efficacy, including vetted offerings from established providers of medical and AHA support. These interventions will follow an integrated approach to motivate users to participate in exercise, cognitively stimulating games and social networking to achieve long-term behavioural change, sustained by continued end user engagement with my-AHA. Scale and Sustainability The proposed platform provides numerous incentives to engage diverse stakeholders, constituting a sustainable ecosystem with empowered end users and reliable standardised interfaces for solutions providers, which will be ready for larger scale deployment at project end. The ultimate aim is to deliver significant innovation in the area of AHA by cooperation with European health care organizations, SMEs, NGOs.

AISym4Med aims at developing a platform that will provide healthcare data engineers, practitioners, and researchers access to a trustworthy dataset system augmented with controlled data synthesis for experimentation and modeling purposes. This platform will address data privacy and security by combining new anonymization techniques, attribute-based privacy measures, and trustworthy tracking systems. Moreover, data quality controlling measures, such as unbiased data and respect to ethical norms, context-aware search, and human-centered design for validation purposes will also be implemented to guarantee the representativeness of the synthetic data generated. Indeed, an augmentation module will be responsible for exploring and developing further the techniques of creating synthetic data, also dynamically on demand for specific use cases. Furthermore, this platform will exploit federated technologies for reproducing un-indentifiable data from closed borders, promoting the indirect assessment of a broader number of databases, while respecting the privacy, security, and GDPR-compliant guidelines. The proposed framework will support the development of innovative unbiased AI-based and distributed tools, technologies, and digital solutions for the benefit of researchers, patients, and providers of health services, while maintaining a high level of data privacy and ethical usage. AISym4Med will help in the creation of more robust machine learning (ML) algorithms for real-world readiness, while considering the most effective computation configuration. Furthermore, a machine-learning meta-engine will provide information on the quality of the generalized model by analyzing its limits and breaking points, contributing to the creation of a more robust system by supplying on-demand real and/or synthetic data. This platform will be validated against local, national, and cross-border use-cases for both data engineers, ML developers, and aid for clinicians’ operations.

MLSysOps will achieve substantial research contributions in the realm of AI-based system adaptation across the cloud-edge continuum by introducing advanced methods and tools to enable optimal system management and application deployment. MLSysOps will design, implement and evaluate a complete framework for autonomic end-to-end system management across the full cloud-edge continuum. MLSysOps will employ a hierarchical agent-based AI architecture to interface with the underlying resource management and application deployment/orchestration mechanisms of the continuum. Adaptivity will be achieved through continual ML model learning in conjunction with intelligent retraining concurrently to application execution, while openness and extensibility will be supported through explainable ML methods and an API for pluggable ML models. Flexible/efficient application execution on heterogeneous infrastructures and nodes will be enabled through innovative portable container-based technology. Energy efficiency, performance, low latency, efficient, resilient and trusted tier-less storage, cross-layer orchestration including resource-constrained devices, resilience to imperfections of physical networks, trust and security, are key elements of MLSysOps addressed using ML models. The framework architecture disassociates management from control and seamlessly interfaces with popular control frameworks for different layers of the continuum. The framework will be evaluated using research testbeds as well as two real-world application-specific testbeds in the domain of smart cities and smart agriculture, which will also be used to collect the system-level data necessary to train and validate the ML models, while realistic system simulators will be used to conduct scale-out experiments. The MLSysOps consortium is a balanced blend of academic/research and industry/SME partners, bringing together the necessary scientific and technological skills to ensure successful implementation and impact.

Non-Communicable Diseases (NCDs) e.g. osteoporosis, osteoarthritis, joint & ligament wear, frailty fractures and associated complications in elderly are a severe threat frequently leading to a permanent decline of the patient’s general health and autonomy. NCD prevention is highly relevant to reduce the need for long-term care. The economic burden is expected to increase by 65% from 2020 to 2040 with a strong negative impact on patient’s quality of life. SmILE will provide a cross-sectoral, early risk detection methodology by holistic analysis of elderly patients’ health data. Co-creation with end-users and consideration of diverse needs, mental and physical abilities, living and socio-economic conditions as well as life-situation of older people are implemented. This includes training and enhanced stream of information to patients and other stakeholders. It will offer an AI-based patient analysis, incl. integration of smart wearables and medical devices incl. implants to support monitoring of general health status. Our approach will disrupt the cycle of frailty by minimizing physical decline and risk of re-fractures. To enhance the AI functions, new data sources will be established by instrumenting medical devices, mainly implants, to augment them into monitoring and actively supporting devices. SMILE will: (1) develop an integrated platform to collect, centralize, manage, analyze and share multimorbid geriatric patient data using technology such as AI & machine learning; (2) establish a pan-European bottom-up model of good governance of health data being patient-centered and patient-controlled in line with current regulations; and (3) establish a solution driven by the needs of citizens and patients of old age. SMILE will establish a new approach of outcome-monitoring to provide patient centered, personalized and integrated treatment of elderly NCD patients to improve quality of life, enable real empowerment and ease the financial burden in an ageing society.