Autoimmune disease affects 10% of adults, most of whom are women, and two of the top five medications with the highest cost globally are used to maintain these recurring conditions in remission. These medications act by suppressing the immune system, leaving the patient exposed to severe infection and at risk of cancer. Affected individuals receive standard treatment regimens for long periods of time, even though, in some cases, the autoimmune response may no longer be detectable. The general requirement for these medications, and their side effects, has been raised as a key target for research by the PARADISE consortium patient groups. Therefore, we aim to develop and validate a deployable personalised predictive tool that will accurately define the individual’s degree of immune system activation so that the medication dose can be tailored and, in some cases, stopped safely. We use systemic vasculitis as an archetypal autoimmune disease, integrating clinical, innovative biomarker and smartphone app-derived patient wellbeing data through a novel semantic web platform to inform predictive algorithms that will underpin a physician-facing tool. Such artificial intelligence (AI) applications are coming under intense scrutiny in the EU, so we will co-develop an “AI transparency notice” with the patient arm of the European Reference Network for immune disorders through a series of multi-stakeholder workshops, which will make explicit and explainable the full provenance of the PARADISE tool clinical outputs.

Haematological diseases (HDs) are a large group of disorders resulting from quantitative or qualitative abnormalities of blood cells, lymphoid organs and coagulation factors. Despite most of them (~74%) are rare, the overall number of HD affected patients worldwide is important, placing a considerable economic burden on healthcare systems and societies. Despite the existence of several collaborative research groups at national and EU level, current clinical approaches are often ineffective, particularly for rarest conditions, due to the relatively low number of patients per disease and the high number of unconnected clinical entities. SYNTHEMA aims to establish a cross-border data hub where to develop and validate innovative AI-based techniques for clinical data anonymisation and synthetic data generation (SDG), to tackle the scarcity and fragmentation of data and widen the basis for GDPR-compliant research in RHDs. The project will focus on two representative RHD use cases: sickle-cell disease (SCD) and acute myeloid leukaemia (AML). SYNTHEMA will develop a federated learning (FL) infrastructure, equipped with secure multiparty computation (SMPC) and differential privacy (DF) protocols, connecting clinical centres bringing standardised, interoperable multimodal datasets and computing centres from academia and SME. This framework will be utilised to train the developed algorithms and perform SMPC-based global model aggregation in a privacy-preserving fashion. The resulting data will be validated for their clinical value, statistical utility and residual privacy risks. The project will develop legal and ethical frameworks to guarantee privacy by-design in the collection and processing of health-related personal data and attain an ethics-wise algorithm co-creation. Project outcomes, including pipelines, standards and data, will be made openly available to stakeholders in the healthcare, academia and industry field, and contribute to existing rare disease registries

Multimorbidity is a common condition in older age, and can substantially influence individuals’ health and quality of life, making management more difficult. A single-disease approach with fragmented care is still prevalent in current healthcare systems despite multimorbidity representing a heterogeneous spectrum of disease combination(s). On this background, the novel approach underpinning the AFFIRMO project is to focus on clusters of multimorbidity where atrial fibrillation(AF) represents one of the chronic conditions. Improving the management of AF in the context of multimorbidity may benefit individuals on a larger scale, with a holistic approach to optimize clinical management of older AF patients taking into account the multifaceted aspects of individuals’ health, including multimorbidity, polypharmacy, personal preferences, and social context. First, the project aims to identify different clusters of multimorbidity in older patients with AF. Second, AFFIRMO aims to assess the needs of patients, caregivers, and health professionals for the comprehensive management of multimorbidity including AF, and to examine ways of optimizing care and self-management. Third, AFFIRMO will develop, implement and test the effectiveness of a patient-centered approach on older multimorbid AF patients in the clinical practice. We aim to adapt, implement and promote a care pathway, in older patients with multimorbidity. A specific objective will be to develop an interoperable care framework that can facilitate the application of this personalized care pathway, that bridges the continuum between primary and secondary care, with the active involvement of patients with shared decision-making. A further aim will be to model the impact of multimorbidity including AF on healthcare costs and the health economic benefits by the proposed integrated care pathway. Finally, subgroup analyses would assess differences on outcomes of in relation to gender and social inequalities.

The Gravitate-Health mission is to equip and empower citizens with digital information tools that make them confident, active, and responsive in their patient journey, specifically encouraging safe use of medicines for better health outcomes and quality of life. It is our vision that engagement of citizens in their own health can only be achieved with access to actionable, understandable, relevant, reliable and evidence-based information that meets their specific needs, health context, and literacy level. This project's ambition is to provide a key piece to advance this vision: the Gravitate Lens (G-lens), which focuses (but does not conceal or filter) approved electronic product information (ePI) content, and offers a route for patients to access trustworthy, up-to-date information that better meet their individual needs. Gravitate-Health is an integrated digital health information project. The principle objective is to demonstrate how use of an integrated, digital, user-centric health information solution with two-way communication could enable tangible improvements in availability and understanding of health information from a set of trusted sources, starting with regulator-approved medicinal product information (e.g. package leaflet content) and EHR-IPS (International Patient Summary). The secondary objectives are to demonstrate that the improved availability and understanding of health information from trusted sources translate to higher levels of adherence to treatment, safer use of medication (Pharmacovigilance), better health outcomes and quality of life, and to develop new and deeper insights into how use of available health information can be optimized to act as effective risk minimization measures. The project allows for efficient and timely development of the G-lens, provides testing grounds for new services and an evaluation framework to test the efficiency, efficacy and safety of Gravitate-Health services. Our main outputs will be an open source digital platform supporting G-lens functionally, demonstrated in a number of testing scenarios, and a White Paper with recommendations on realistic strategies to strengthen access, understanding and future use of digital services like ePIs as a tool for Risk Minimization. The Gravitate-Health is a public – private partnership with 45 members from Europe and the US, co-led by University of Oslo (coordinator) and Pfizer (industry lead), funded by the Innovative Medicines Initiative (IMI) – a joint undertaking of the European Commission, the European Federation of Pharmaceutical Industries and Associations (EFPIA), IMI Associated Partners.

BIGPICTURE, a pathology-led consortium, has the vision to become the catalyst in digital transformation in Pathology. Our mission is to create the first European GDPR compliant platform, in which both quality-controlled Whole Slide Imaging (WSI) data and advanced Artificial intelligence (AI) algorithms will exist. The BIGPICTURE platform will be built on existing assets of ELIXIR EU data infrastructure, including the federated European Genome-phenome Archive (EGA) technology for managing the exchange of confidential information between contributors and users. The consortium will use Cytomine, an established open-source, cross-platform framework to develop unique tools for access to WSI, including annotations and visualisation of algorithm results, while we will develop new and generic models to facilitate AI development and mining of WSI data. By engaging and building consensus with all the relevant stakeholders, we will contribute to the development of a regulatory framework for digital slides and AI-based methods. Finally, BIGPICTURE envisions sustainability of its platform through a community- based model which relies on reciprocity, value creation and inclusiveness. To achieve our vision, we have brought together Europe’s leaders in the field of computational pathology who have access to national and European high-performance computing infrastructures as well as Europe’s fully digitalised pathology departments. Additionally, the consortium has currently access to approximately 4.5 million clinical WSI covering a wider range of indications through 17 partners and 23 third parties from the largest European and international pathology and trial groups. Our consortium is further strengthened by the presence of the European Society of Pathology, Digital Pathology Association, FDA and 9 SMEs as partners, while we are further supported by professional societies, and patient advocates.