Background Health systems face a time of unprecedented change, with spiraling costs, increasing cultural disparity in access to healthcare and research, and an infrastructure that is decades old. Today, telehealth is a realistic alternative making care and research more accessible and personalised with less burden to better support the most vulnerable and under-served in our society. The ability to test and monitor for illnesses using Patient Centric micro-Sampling (PCmS) is at the centre of this reform. Aim and main objectives This project is designed to build upon existing pilots and knowledge, then collaborate cross-sectorially to co-create and test the logistics, infrastructure and tools required to make PCmS a core healthcare tool and an acceptable alternative to venous blood-draw across Europe. This project aligns with many IHI’s objectives focusing on cross-sectorial collaboration, emphasizing patient and end-user- centric co-design of outputs, harmonised regulatory and data generation approaches enhancing the potential of digital innovations in healthcare, while aiming to reduce the environmental footprint during the project and in final outputs to ensure that the expected long-term impact is a reachable reality that will deliver significant benefit to the community and address unmet public health needs at scale. To achieve our objectives, we bring together a broad group of required expertise, know-how and end-users (i.e., public and patients) to form a public-private-partnership specifically equipped to tackle this challenge. This collaborative approach where the relevant stakeholders such as healthcare professionals, regulatory agencies and patients are involved and integrated to deliver solutions and innovation across healthcare systems and ensure the best chances for success and long-term positive impact from this project. Key deliverables include: 1) An optimized, tested and validated ‘Gold Standard’ infrastructure and workflow for PCmS across Europe as a proven and reliable alternative to venipuncture 2) Harmonised and clear regulatory and HTA pathways, standards and acceptability, measures and cost-benefit models across Europe 3) Documented evidence to draw a citable ‘line in the sand’ for future research to support decisions to integrate PCmS into decentralised trials and care pathways 4) Stakeholder engagement and patient involvement models and research on preferences and acceptability for PCmS 5) Foundation for future: Enable access to the developed PCmS scientific findings, tools and assessment measures for rapid uptake and integration of PCmS approaches into decentralised clinical studies and healthcare Expected impact: - Patient-centric microsampling becomes an accepted alternative to the current standard of care venipuncture and the data gathered can be leveraged in healthcare planning. - Lowered patient burden and lowered barrier to access in situations where blood samples need to be collected, whether as part of diagnosis, care plan, health monitoring etc. - A solution to leverage high amounts of data gathered from increased testing can be explored already in this project so that it can pave the way for future research that can improve health outcomes.

The central PANCAIM concept is to successfully exploit available genomic and clinical data to improve personalized medicine of pancreatic cancer. PANCAIM’s concept is unique as it integrates the whole spectrum of genomics with radiomics and pathomics, the three future pillars of personalized medicine. The integration of these three modalities is very challenging in the clinic, but also with AI. PANCAIM uses an explainable, data-efficient, two-staged AI approach. AI biomarkers transform the unimodal data domains into interpretable likelihoods of intermediate disease features. A second AI layer merges the biomarkers and responds with an integrated assessment of prognosis, prediction and monitoring of therapy response, to assist in clinical decision making. PANCAIM builds on four key concepts of AI in Healthcare: data providers, clinical expertise, AI developers, and MedTech companies to connect to data and bring AI to healthcare. Data quantity and quality is the main factor for successful AI. Partners provide eleven Pan European repositories of almost 6000 patients that are open to ongoing accrual. SME Collective Minds builds the GDPR data platform that hosts the data and provides a trustable connection to healthcare for even more and sustainable data. SME TheHyve builds tooling to connect to more genomic repositories (EOSC Health). Six Pan European academic centers provide clinical expertise across all modalities and help realize a curated, high quality annotated data set. Partners also include expert AI healthcare researchers across all clinical modalities with a proven track record. Finally, Siemens Healthineers provides their AI expertise and tooling to bring AI into healthcare for clinical validation and swift clinical integration in 3000 health care institutes.

The goal of EuCanImage is to build a highly secure, federated and large-scale European cancer imaging platform, with capabilities that will greatly enhance the potential of artificial intelligence (AI) in oncology. Firstly, the EuCanImage platform will be populated with a completely new data resource totaling over 25,000 single subjects, which will allow to investigate unmet clinical needs like never before, such as for the detection of small liver lesions and metastases of colorectal cancer, or for estimating molecular subtypes of breast tumours and pathological complete response. Secondly, the cancer imaging platform, built by leveraging the well-established Euro-Bioimaging infrastructure, will be cross-linked to biological and health repositories through the European Genome-phenome Archive, allowing to develop multi-scale AI solutions that integrate organ-level, molecular and other clinical predictors into dense patient-specific cancer fingerprints. To deliver this platform, the consortium will build upon several key European initiatives in data sharing for personalised medicine research, including EUCANCAn (cancer genomics and health data sharing), euCanSHare (cardiac imaging and omics data sharing) and EUCAN-Connect (federated data analytics). Furthermore, to foster international cooperation and leverage existing success stories, the consortium comprises the coordinators of The Cancer Imaging Archive (TCIA), the US cancer imaging repository funded by the National Cancer Institute. This will allow EuCanImage to leverage a unique 10-year long experience in cancer imaging storage, anonymisation, curation and management. Finally, a close collaboration between world renown clinical, radiomics, AI and legal experts within the consortium and beyond will establish well-needed guidelines for AI development and validation named FUTURE, for delivering Fair, Universal, Traceable, Usable, Robust and Explainable decision support systems for future cancer care.

Atrial fibrillation (AF) and stroke are major health care problems in Europe. They are most often the clinical expression of atrial cardiomyopathy, which is under-recognised due to the lack of specific diagnostic tools. Multidisciplinary research and stratified approaches are urgently needed to prevent, diagnose, and treat AF and stroke and preempt the AF-related threat to healthy ageing in Europe. MAESTRIA is a European consortium of 18 clinicians, scientists and Pharma industrials who are at the forefront of research and medical care of AF and stroke patients. It will create multi-parametric digital tools based on a new generation of biomarkers that integrate artificial intelligence (AI) processing and big data from cutting edge imaging, electrocardiography and omics technologies. It will develop novel biomarkers, diagnostic tools and personalized therapies for atrial cardiomyopathy. Digital Twin technologies, a rich data integrator combining biophysics and AI will be used to generate virtual twins of the human atria using patient-specific data. Unique experimental large-animal models, ongoing patient cohorts and a prospective MAESTRIA cohort of patients will provide rigorous validation for new biomarkers and newly developed tools. A dedicated core lab will collect and homogenize clinical data. MAESTRIA will be organized as a user-centered platform, easily accessible via clinical parameters routinely used in European hospitals. A Scientific Advisory Board comprising potential clinician users will help MAESTRIA meet clinical and market needs. Dissemination and visibility of the MAESTRIA consortium mission will benefit from participation of the German Competence Network on Atrial Fibrillation (AFNET), and support from the European Society of Cardiology, clinicians, scientists, and other professional societies. MAESTRIA will be ready to tackle the major challenges of data integration and personalized medicine focused on atrial cardiomyopathy, AF and stroke.

UMBRELLA is a holistic approach to progress, reshape, and benchmark the overall stroke care pathway and set new and improved standards of care in terms of primary and secondary prevention, rapid access to treatments, early accurate diagnosis, stratification, management and real-time monitoring, therapeutic targets identification, and rehabilitation, recurrent stroke and related cardiovascular events. This innovative approach will transform healthcare systems by improving and harmonizing professionals' workflows in a more patient-centred, digitalized, and communicative manner. UMBRELLA aims to revolutionize stroke management by implementing a comprehensive approach that addresses gaps along the whole continuum of the stroke care pathway. The key paradigm in the project is the multicentric, synergistic "umbrella" strategy for local data collection, harmonization, and standardization along the entire pre-, in-, and post-hospitalization pathway. By establishing specific common data models (CMDs) implemented in each of the 7 top-tier European clinical centres, UMBRELLA will create a federated data platform (U-platform) where Real World Data (RWD)-based AI algorithms can be locally created and validated, to advance personalised diagnosis, risk prediction, and treatment decisions in the acute and post-acute phases of stroke. The algorithms will be then trained in a decentralized manner through a federated learning infrastructure (FL-platform), which preserve data security and privacy, avoiding data centralization or exchange across centres but fostering collective AI-models training. On the other hand, standardized stroke management protocols and procedures will be created and implemented across the participating centres, including the validated usage of advanced digital technologies as solutions to facilitate data collection, visualization, patient engagement, monitoring, outcomes integration, and decision-making across the whole stroke pathway.