Assessing the Behavioral Intention of Individuals to Use an AI Doctor at the Primary, Secondary, and Tertiary Care Levels

Abstract

The study examines the behavioral intention to use an AI doctor at the individual level at primary, secondary, and tertiary care levels. The research model has been designed according to the unified theory of acceptance and use of technology to understand the acceptance and use of an AI doctor at the individual level. The causal comparison screening approach, which is used to identify the causes and effects of people’s attitudes, behaviors, ideas, and beliefs, has been employed. This study utilized a hybrid analysis methodology combining two-phase analysis using partial least squares structural equation modeling and evolving artificial intelligence named deep learning (Artificial Neural Network) on 432 usable responses. The first step was using structural equation modeling to examine the hypotheses; then the nonlinear interactions between the variables have been examined using an artificial neural network. According to the analysis results, perceived task-technology fit, perceived privacy, performance expectancy, and social influence constructs affecting the intention to use an AI doctor in primary, secondary, and tertiary levels of care are the main constructs. A strong behavioral intention exists at all levels of healthcare, primary, secondary, and tertiary, to use an AI doctor for individual-level healthcare.