Diversity in people's reluctance to use medical artificial intelligence: Identifying subgroups through latent profile analysis

Haixia Wang; Qiaoqiao Sun; Li Gu; Kaisheng Lai; Lingnan He

doi:10.3389/frai.2022.1006173

Diversity in people's reluctance to use medical artificial intelligence: Identifying subgroups through latent profile analysis

Front Artif Intell. 2022 Oct 6:5:1006173. doi: 10.3389/frai.2022.1006173. eCollection 2022.

Authors

Haixia Wang¹, Qiaoqiao Sun², Li Gu³, Kaisheng Lai¹, Lingnan He⁴

Affiliations

¹ School of Journalism and Communication, Jinan University, Guangzhou, China.
² Guangdong Medical Doctor Association, Guangzhou, China.
³ School of Innovation Design, Guangzhou Academy of Fine Arts, Guangzhou, China.
⁴ School of Communication and Design, Sun Yat-sen University, Guangzhou, China.

Abstract

Medical artificial intelligence (AI) is important for future health care systems. Research on medical AI has examined people's reluctance to use medical AI from the knowledge, attitude, and behavioral levels in isolation using a variable-centered approach while overlooking the possibility that there are subpopulations of people who may differ in their combined level of knowledge, attitude and behavior. To address this gap in the literature, we adopt a person-centered approach employing latent profile analysis to consider people's medical AI objective knowledge, subjective knowledge, negative attitudes and behavioral intentions. Across two studies, we identified three distinct medical AI profiles that systemically varied according to people's trust in and perceived risk imposed by medical AI. Our results revealed new insights into the nature of people's reluctance to use medical AI and how individuals with different profiles may characteristically have distinct knowledge, attitudes and behaviors regarding medical AI.

Keywords: behavioral intention; medical AI; negative attitude; objective knowledge; subjective knowledge.