JMIR Publications

ABSTRACT

Background:

The emergence of Artificial Intelligence (AI) has been proven beneficial in several healthcare areas. Nevertheless, the uptake of AI in healthcare delivery remains poor. Despite the fact that the acceptance of AI-based technologies among medical professionals is a key barrier to their implementation, little is known about what informs such attitudes.

Objective:

The aim of this study was to identify and examine factors that influence the acceptability of AI-based technologies among medical professionals.

Methods:

To collect data, a survey was developed based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, which was extended by adding the predictor variables perceived trust (PT), anxiety (AN) and innovativeness (IN) and the moderator profession. The web-based survey was completed by 67 medical professionals in the Netherlands. The data were analyzed by performing a multiple linear regression followed by a moderating analysis using Hayes’s PROCESS macro (SPSS IBM version 26.0).

Results:

Multiple linear regression showed that the model explained 75,4% of the variance in the acceptance of AI-powered care pathways (〖adjusted R〗^2=.754; F=22.548, P=.000). The variables medical performance expectancy (beta=.465; P=.000), effort expectancy (beta=-.215; P=.005), perceived trust (beta=.221; P=.007), non-medical performance expectancy (beta=.172; P=.077), facilitating conditions (beta=-.160; P=.005) and professional identity (beta=.156; P=.060) were all identified as significant predictors of acceptance. Social influence of patients (beta=.042; P=.627), anxiety (beta=,021; P=.837) and innovativeness (beta=.078; P=.300) were not identified as significant predictors. A moderating effect by gender was found between the relationship of facilitating conditions and the acceptance (beta=-.406; P=.088).

Conclusions:

Medical performance expectancy was the most significant predictor of AI-powered care pathway acceptance among medical professionals. Non-medical performance expectancy, effort expectancy, perceived trust and professional identity were also found to significantly influence the acceptance of AI-powered care pathways. These factors should be addressed for successful implementation of AI-powered care pathways in healthcare delivery. The study was limited to medical professionals in the Netherlands, where uptake of AI technologies is still in an early stage. Follow-up multinational studies could further explore the predictors of acceptance of AI-powered care pathways over time and in different geographies.