Carotid artery disease, the primary trigger of ischaemic cerebrovascular events including stroke, causes major morbidity, mortality and healthcare costs worldwide. Still, treatment is based on criteria established in the 90s that do not take into account the molecular evolution we have witnessed since, nor the introduction of new medication, leading to remarkably high unnecessary surgical treatment while missing most patients at risk. TAXINOMISIS will provide novel disease mechanism-based stratification for carotid artery disease patients to address the needs for stratified and personalised therapeutic interventions in the current era. This will be achieved through (1) the dissection of mechanisms mediating carotid artery disease, and identification of susceptibility and protection factors of plaque erosion and/or rupture using longitudinal cohorts and multi-omics, (2) the definition of distinct disease phenotypes and endotypes, and generation of molecular fingerprints of high versus low-risk states through systems medicine, (3) the development of a multilevel risk prediction model of the symptomatic plaque incorporating new biomarkers and advanced imaging, implemented in a software, to assist patient stratification and clinical decision making, (4) the development of novel pharmacogenomics solutions based on lab-on-a-chip technology to support personalized treatment, (5) the evaluation of the new risk prediction model and lab-on-a-chip device in a prospective observational clinical study, and (6) the assessment of regulatory, cost-effectiveness and ethical issues towards the implementation and commercialization of the programme’s outcomes. TAXINOMISIS has therefore the potential to rationally change the current state-of-the-art in the stratification of patients with carotid artery disease by reducing unnecessary operations, refining medical treatment and opening up new avenues for therapeutic intervention, while strengthening the European biotechnology sector.

Heart failure (HF) is a pandemic currently affecting up to 15 million people in Europe. It is a complex clinical syndrome presenting with impaired heart function and is associated with poor quality of life for patients and high healthcare costs. There is a high clinical demand for novel artificial intelligence (AI) tools which will facilitate risk stratification, early diagnosis, and disease progression assessment in HF. Such tools are essential to allow prompt initiation of evidence-based prevention and treatment strategies which will improve patient quality of life, reduce morbidity and mortality and the HF burden on healthcare. STRATIFYHF aims to develop, validate and implement the first AI-based, decision support system (DSS) for risk stratification, early diagnosis, and disease progression assessment in HF to accommodate both primary and secondary care clinical needs. The DSS will integrate patient-specific demographic and clinical data using existing and novel technologies and establish AI-based tools for risk stratification and HF prediction using machine learning. Additionally, a mobile app will be developed to empower patients to better manage their condition, and health care professionals to make informed decision in selection of evidence-based HF prevention and treatment strategies. Our multidisciplinary consortium, including three small-to-medium enterprises (SMEs) and two stakeholder organisations, will be guided by medical advice and regulatory and health technology experts to deliver the DSS as a medical class 2b device, reaching TRL 8 by the end of the project. STARTIFYHF will change the way in which HF is diagnosed today and thereby improve the quality and length of patients’ lives and lead to efficient and sustainable healthcare systems by reducing the number of HF-related hospital admissions and unnecessary referrals from primary to secondary care in Europe and beyond.

TeleRehaB DSS targets the promotion of AI adoption in everyday clinical practice for balance rehabilitation training. An AI-based decision support system (DSS) will be developed expanding upon the existing Augmented Reality (AR) rehabilitation training platform, with its balance exercises, exergames, cognitive training and remote patient monitoring with wearables and IoT devices from HOLOBALANCE project (TL6), to provide suggestive feedback for experts through the entire clinical rehabilitation pathway. The first component of AI models of TeleRehaB DSS will assess prognostic factors for risk of falls, treatment effectiveness, outcomes and side effects at baseline level, using a high volume of retrospective data for initial training. The other AI pillar of TeleRehaB DSS will introduce automated balance intervention planning and management functionality. The DSS will provide for each patient an optimal set of personalised rehabilitation activities, considering the best clinically effective treatment in conjunction with socio-economic effectiveness, and eHealth literacy. The later will be evaluated with a quick and easy to use tool with simple tasks to assess patient's level of technological awareness (i.e. use of smart devices, AR and IoT equipment), in order to predict if this is going to affect compliance and adherence with interventions that rely on the use of such novel technologies. Finally, the most beneficial use of AI in TeleRehaB DSS will consist of automated remote patient monitoring with wearables and IoT sensing devices, allowing rehabilitation training programs to be performed at home. The DSS will evaluate in real-time patient performance, symptoms occurrence with virtual AR physio's providing corrective and motivational feedback as activities are performed. These performance evaluation measures will be fed back to the DSS to support experts with their most time and effort-consuming activities of day-to-day patient management.

The smart healthcare domain utilizing a combination of Artificial Intelligence (AI) and medical Internet of Things devices is undoubtedly transforming the healthcare industry as it can deliver new applications and solutions that benefit patients, doctors and hospitals. Improved treatment, cost reduction and faster diagnosis are some of the advantages that smart healthcare brings to healthcare stakeholders. First, AI can be utilized to efficiently process data for improved disease diagnosis in medical images including liver lesion classification and segmentation, brain tumor segmentation, breast cancer detection, etc. Besides, AI can assist in securing the healthcare system from cybersecurity attacks that target the operation of medical IoT devices or sensitive medical data. However, the common assumption with AI is that the training, testing and deployment environment is benign and trustworthy. This assumption, however, does not hold true in general. As a matter of fact, research in this area has shown that small perturbations in the important features of AI models during training or testing phase can trivially undermine their performance. This gives rise to adversarial AI, in which attackers can trick healthcare AI models to degrade their diagnostic or cybersecurity detection performance. ANTIDOTE project objective is to create a sustainable European and inter-sectoral network of organizations working on a joint research programme in the interdisciplinary fields of Healthcare, AI and Cybersecurity. The participants will exchange skills and knowledge which will allow them to design and develop concrete mechanisms to evaluate the robustness of AI models and propose novel methods to ensure their secure, safe, resilient and robust operations in the healthcare domain. The outcomes of the ANTIDOTE project will have a significant benefit for European society, while strengthening the collaborative research between the different countries and sectors.