Analysis of trust factors for AI-assisted diagnosis in intelligent Healthcare: Personalized management strategies in chronic disease management

By utilizing various techniques to integrate the Unified Theory of Acceptance and Use of Technology (UTAUT) model with the variables of personal innovation and task complexity, an initial trust mechanism is established. Based on the inherent motivation and demand of doctors to use AI-assisted diagnosis systems, a doctor adoption model for AI-assisted diagnosis systems is established. Using chronic disease management as an example, personalized management strategies based on reinforcement learning technology are discussed for a virtual management environment that provides intervention recommendations according to the medical condition. It is shown in experiments that the fitting goodness-of-fit value of the model for willingness to use, which excludes initial trust-related factors, is only 0.557, while the fitting goodness-of-fit value of the proposed model in this paper is 0.673. This indicates that the model that includes initial trust factors can more accurately explain the logic of the behavior evolution of doctors adopting AI-assisted diagnosis systems.