Author(s)

Yuan Fu

Corresponding Author

Yuan Fu

Abstract

In the context of HIV/AIDS prevention and control, patient self-management is of paramount importance. The advancement of artificial intelligence (AI) has introduced a new opportunity to enhance the multidimensional self-management capabilities of individuals living with HIV through cross-scenario AI intervention models. This paper seeks to investigate the application and optimization of such models in the multidimensional self-management of HIV patients. First, this paper gives a theoretical foundation based on health behavior theory and human-computer interaction. Second, it develops a comprehensive self-management framework for HIV patients that encompasses multiple dimensions. This paper elaborates on the implementation of cross-scenario AI interventions, including mHealth and AI chatbot interventions, as well as family-centered AI support systems. Furthermore, it highlights some significant problems encountered in practical applications, such as the algorithm's insufficient reliability and the risk of sensitive patient data being leaked. At the same time, it also proposes corresponding improvement methods to address these issues. These methods include utilizing multimodal data fusion, implementing more robust privacy protection measures, engaging users in the development process, and effectively integrating these AI tools into doctors' daily workflows. Finally, this paper concludes that optimizing these AI intervention models that can be used in different scenarios can greatly help HIV-infected people improve their self-management ability, which can provide important support for our whole society's efforts to fight AIDS.

Keywords

Cross-scene artificial intelligence intervention mode; HIV patients; Multidimensional self-management