Initiative overview
The SOTE AI Vision 2035 is built around four principles: individual self-direction, active prevention for populations and individuals, personalised medicine and tailored treatments, and automation of care, services, decision-making and support functions. Over five to ten years, healthcare and social welfare AI is expected to evolve from supporting professionals to becoming collaborative intelligence and increasingly autonomous automation that initiates, directs and supervises entire service and care processes. Citizens and clients are increasingly expected to utilise AI themselves to maintain their wellbeing, with a personal AI wellbeing coach acting as a guide to services, initially with simple features but evolving into a more versatile and interactive assistant. Self-measurement is noted as needed to provide comprehensive data for training AI models, enabling AI to produce personal forecasts and tailored care pathways.
The roles of healthcare and social welfare professionals will diversify, with more time freed from routine tasks for direct client work. Professionals may specialise either in physical, in-person care requiring direct interaction or in remote, knowledge-based supervisory roles monitoring and guiding clients' health as well as the activities of AI agents and robots in service provision. Leadership is described as increasingly about managing the technological transformation within the system, with leading people through change becoming a key skill as knowledge-based management and services become more automated.
The recommended actions are organised around five areas: national and regional guidance and legislation, including identifying opportunities for autonomous AI decisions and necessary legislative amendments; education and competence for professionals, leaders and citizens, including guaranteeing access to AI for all through devices, connections and AI literacy; standardising healthcare and social welfare data structures and facilitating their shared use for developing large AI models; identifying service and care paths suitable for automation and developing the use of AI in genetic data analysis and disease prediction; and supporting public-private collaboration and reforming procurement. A Wellbeing AI Ombudsperson is proposed to oversee the rights of clients and professionals and to monitor impact, equity, ethics and environmental effects in the long term.
