Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
