Psoriatic Arthritis (PsA) is a chronic, progressive, inflammatory disease affecting 1-2% of the general population, while manifesting in up to 30% of people with psoriasis (PsO). The transition from health to PsA is currently untraceable; diagnosis of early PsA is challenging even in PsO patients. Untimely diagnosis is common and contributes to early deterioration of quality of life, also increasing the burden of the multiple comorbidities associated with PsA. In this vein, iPROLEPSIS aspires to shed light upon the health-to-PsA transition with a comprehensive multiscale/multifactorial PsA model employing novel trustworthy AI-based analysis of multisource and heterogenous (i.a., in-depth health, environmental, genetic, behavioural) data, digital phenotyping of inflammatory symptoms with emphasis on tracking of motor manifestations using smart devices and wearables, novel optoacoustic imaging-based markers of PsA in the skin and joints, and investigation of the role of mast cells in the PsA transition, to identify key drivers of the disease and support personalized models for PsA risk/progression prediction and monitoring as well as associated inflammation detection and severity assessment. To ultimately advance PsA diagnosis and care, the models will be translated into a digital health ecosystem comprising dependable tools for supporting healthcare professionals in disease screening, monitoring and treatment via quantitative, explainable evidence, and empowering people with/at risk of PsA with tailored insights and preventive interventions based on actionable factors for educated health management. The project will steer its research and development efforts following a trustworthy framework for ethical, lawful, and robust AI, and a user-centered co-creation approach based on constant involvement of key stakeholders during the design, development, and testing of the digital health ecosystem, securing successful integration of the latter in the continuum of care.

In healthcare, assistive health technology plays a pivotal role as a catalyst for positive transformation. These innovative solutions are crafted to improve health outcomes and treatment regimens and address the challenges of various diseases. Their overarching mission is to enhance the overall quality of life while ensuring equal access to and participation in daily activities for everyone, encompassing patients with various health conditions, the expanding elderly population, and even individuals navigating an increasingly automated and demanding work environment. Outstanding instances of assistive health technology include home-based rehabilitation robotics that target sensorimotor deficits, electric wheelchairs providing individuals with independent mobility, telehealth applications facilitating cardiovascular monitoring, and powered prostheses that reinstate mobility and instil confidence in everyday activities. Despite recent strides in hardware, sensing, control systems and actuation technologies, notable limitations persist. These systems often encounter challenges when operating beyond their specifically tailored environments, necessitating manual adjustments and limiting safety and reliability in dynamic scenarios. We aim to push assistive health technology towards intelligent, safe, and reliable assistance that mirrors human interaction. This entails harnessing the latest ML advancements for real-world human-engineering system interactions. We are committed to cultivating a new generation of multidisciplinary doctoral candidates to realise this vision. Through close collaboration with industry partners, and assistive health technology developers, these candidates will gain knowledge from diverse disciplines, encompassing sensing, control, and safety considerations. The ultimate outcome will be assistive health technologies that elevate health diagnoses, treatments, and the overall quality of life, ultimately fostering equity in daily activities.