Assess Knowledge of Healthcare Technologies and informatics enabled by Artificial Intelligence

Abstract

Background: Healthcare is anticipated to increasingly incorporate artificial intelligence (AI) technologies into patient care. Recognizing public perceptions of these tools is crucial to their effective development and adoption. This exploratory study assessed participants' openness, concerns, and perceived advantages of AI-driven healthcare technologies and informatics, alongside socio-demographic, health-related, and psychosocial factors influencing these views.

Methods: A measurement tool was developed to represent six AI-based technologies that diagnose, predict, or suggest treatment options. This tool was deployed via an online survey completed by adults (N=936). Participants rated their openness toward each AI technology in healthcare contexts, as well as concerns and perceived benefits associated with each. Additionally, socio-demographic data, health status, and psychosocial factors such as trust in the healthcare system and technology were collected. Exploratory and confirmatory factors analyses of concern and benefit items revealed two main factors: overall levels of concern and perceived benefit. Descriptive statistics captured levels of openness, concern, and perceived benefit, while correlational analyses examined relationships between socio-demographic, health, and psychosocial variables and these perceptions. Concurrent associations were assessed using multivariable regression models.

Results: Participants expressed moderate openness to AI-driven healthcare technologies and informatics   (M=3.1/5.0 ±0.9), with varying degrees of acceptance depending on the type of AI application. Statements of concern and benefit significantly influenced participants' views. Trust in healthcare systems and technology consistently emerged as strong correlates of openness, concern, and perceived benefits. Although other socio-demographic, health-related, and psychosocial factors showed weaker or negligible associations, multivariable models identified modest links between perceptions and variables like personality traits (e.g., conscientiousness, agreeableness), employment status, age, sex, and race.

Conclusions: Participants’ openness to AI in healthcare appears tentative, implying that early engagement and exposure to AI technologies could substantially shape attitudes, especially as these technologies either reinforce or erode trust. Given the exploratory findings, further research is needed to clarify these relationships.