Social AI for a Healthier Lifestyle: Four Competencies to Manage and Prevent Chronic Diseases
bookPart
Lifestyle-related diseases like type 2 diabetes mellitus (T2DM) and chronic obstructive pulmonary disease (COPD), have a major impact on society, asking for comprehensive disease management support. While AI technology has advanced for diagnosis and disease detection, its implementation into eHealth and mHealth applications remains limited, with low adoption rates and limited evidence of effectiveness. To achieve the necessary levels of client engagement and self-efficacy in chronic disease lifestyle management (CDLM), Artificial Intelligence (AI) support must demonstrate social competencies throughout its entire lifecycle—an under-researched topic. This paper introduces a novel Social AI Competence framework designed to provide durable personalized CDLM-support. The framework defines four complementary core competencies: (1) supporting meaningful activities, (2) providing responsible actionable explanations, (3) engaging persons in reflective interactions, and (4) strengthening and leveraging support networks. Underlying these competencies are eleven key social skills, detailed in terms of their foundation, functionality, state-of-the-art advancements, and research and development challenges. The CDLM system under development employs interactive modeling techniques to incorporate the experience and expertise of both experts and clients into these skills, supported by a modular architecture that ensures adaptability and scalability. Integrating social AI functions into the competency framework enables systematic assessment and optimization of their proportional effectiveness in real-world use cases.
TNO Identifier
1018137
ISBN
978-1-64368-611-0
Source title
Proceedings of the 4th International Conference on Hybrid Human-Artificial Intelligence
Editor(s)
Pedreschi, D.
Milano, M.
Tiddi, I.
Russell, S.
Boldrini, C.
Pappalardo, L.
Passerini, A.
Wang, S.
Milano, M.
Tiddi, I.
Russell, S.
Boldrini, C.
Pappalardo, L.
Passerini, A.
Wang, S.
Pages
317-332