The vision of the QoLEAD is to tackle the challenges of dementia by harnessing the learning and predictive power of AI to tailor interventions and deliver warm, smart care solutions with PwD themselves at the steering wheel to drive, focus, and support these innovations. User perspectives will determine the nature of the innovations to improve QoL in three core domains: health, safety and quality of care; participation and social contact; autonomy, meaningfulness and self-esteem. To overcome the implementation gap, QoLEAD will set up an open ecosystem of cooperation with existing field labs and Academic Collaboration Labs at its heart that will iteratively implement knowledge/innovations in practice and offer a learning environment on Dementia & Technology in a unique interdisciplinary collaborative network. The consortium emerged from the Joint AI Network (JAIN), combining state-of-the-art AI technologies with dementia expertise. The ultimate impact of QoLEAD will be improved QoL with better togetherness, resilience, and perseverance to manage care and support along the patient journey optimally.
The specific contribution of Radboud University to this project is to develop a Social AI technology framework using the empowerment methodology SPAN+ and the Social Production Function (SPF) Theory. In addition to the conversational agent technology, the envisioned framework includes virtual humans as interaction means, cloud computing, machine learning, symbolic modelling and reasoning, machine learning and (wearable or home automation) sensing. The framework will support state-of-the-art functionalities for fairness (prevention and mitigation of biases) and explainability of agent decisions. These technologies will be advanced and combined, in co-creation with the stakeholders, into a SAI framework for “blended” care (i.e. care from care professionals as well as informal carers, supported by social AI), aiming at an optimal Quality of Life of the PwD on domains as autonomy, meaningfulness and self-esteem. The personalized, enduring and adaptive support will improve over time based on the data-driven learning technologies.
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