IMPROVE will use Patient Generated Health Data (PGHD) gathered via m-health and e-health technologies to gain improved insights into the real-life behavior of, and challenges faced by, patients of all ages with complex, chronic diseases and comorbidities. Already today, a wealth of patient and citizen information is available, but fragmented, and therefore not coming to its full utility and value. These personal data will complement and improve existing approaches for Patient-Centered Outcome Measures beyond those currently available in state-of-the-art platforms. The IMPROVE platform that the consortium will build will enable the smart use of patient input and patient generated evidence to 1) advance the role of patient preference and patient experience in the context of treatment selection, 2) improve medical device design based on patient preferences and experiences, and 3) facilitate faster market entry of patient-centric and cost-effective advanced integrated care solutions. Improved clinical adoption of Value Based Health Care, and enhanced return on research and innovation investments will be demonstrated in different care settings across the EU, for 10 use cases in at least 5 different disease areas (e.g., ophthalmology, oncology, cardiovascular disease, chronic inflammation, and neurology). The use cases will be conducted using a large variety of implementation strategies, building on a design thinking approach, to optimally test the innovative framework of data gathering and translation into controlled change and action. In addition, a significant contribution from implementation science is planned to reach out to all stakeholders that are relevant for this initiative and maximise the impact to IMPROVE healthcare provision.

EU-TRAINS aims to reinforce the supply chain on sensors for biomechanics and cardiovascular system real-time monitoring targeting applications in the fields of fitness and healthcare. It leverages from the strength of EU digital microsystem and design to support a 100% made-in-Europe supply chain of solutions which encompass smart-textile integration as well as advanced AI-based edge-cloud data processing. In details the following outcomes are foreseen: - Textile integrated electronic systems for real-time monitoring of hearth, respiratory and movement parameters on-the-air and in-water through an interdisciplinary approach; - Semiconductor technologies which allow the re-use of micro-nano systems both in the sports and in the healthcare sectors; - Miniaturized devices allowing the capturing of bio-chemical parameters able to withstand harsh ambient conditions such as salt fogs, chlorine, detergents, high and low temperatures, etc. The following key activities are targeted: - Development, prototyping and demonstration of versatile sensors with edge AI features for improved precision and reliability, that can also be integrated in textiles as well as in smart wearable wrist-watches and in sport equipment and gears targeting also underwater applications; - Cloud-edge Artificial Intelligence combined approaches for reliable diagnosis of body parameters. This will comprise sensor’s self-learning, remote update, multi-sensing approaches based on sensor arrays; - Novel materials that support electronics printing in textiles with stretchability and self-healing capabilities. Societal benefits are foreseen in the transition to a healthier lifestyle by promoting regular physical activity through affordable tools and services for a large audience, including people with disabilities. Moreover, this will impact the smart/remote-healthcare sector which will benefit of the availability of low-cost microfabricated solutions for intelligent, versatile, connected body sensors.

The increasing amount and availability of collected data (cancer imaging) and the development of novel technological tools based on Artificial Intelligence (AI) and Machine Learning (ML), provide unprecedented opportunities for better cancer detection and classification, image optimization, radiation reduction, and clinical workflow enhancement. The INCISIVE project aims to address three major open challenges in order to explore the full potential of AI solutions in cancer imaging: (1) AI challenges unique to medical imaging, (2) Image labelling and annotation and (3) Data availability and sharing. In order to do that INCISIVE plans to develop and validate: (1) an AI-based toolbox that enhances the accuracy, specificity, sensitivity, interpretability and cost-effectiveness of existing cancer imaging methods, (2) an automated-ML based annotation mechanism to rapidly produce training data for machine learning research and (3) a pan-European repository federated repository of medical images, that will enable the secure donation and sharing of data in compliance with ethical, legal and privacy demands, increasing accessibility to datasets and enabling experimentation of AI-based solutions. The INCISIVE models and analytics will utilize various cancer imaging scans, biological data and EHRs, and will be trained with 1 PB of available data provided by 8 partners within the project. INCISIVE solution will be investigated in four validation studies for Breast, Prostate, Colorectal and Lung Cancer, taking place in 8 pilot sites, from 5 countries (Cyprus, Greece, Italy, Serbia and Spain), with participation of at least 2,600 patients and a total duration of 1.5 year. INCISIVE moves beyond the state of the art, by improving sensitivity and specificity of lower cost scanning methods, accurately predicting the tumor spread, evolution and relapse, enhancing interpretability of results and “democratizing” imaging data.