Abstract
Background: The purpose of this study was to investigate the general population’s view on AI in medicine with specific emphasis on three areas that have experienced major progresses in AI research in the past few years, namely radiology, robotic surgery and dermatology. We hypothesized, based on optimistic views posed in the media about AI in medicine, that the general population would have higher levels of trust than distrust of AI in medicine. Methods: For this prospective study the April 2020 online LISS (Longitudinal Internet Studies for the Social sciences) Panel wave was used. The LISS panel is a nationally representative household panel study for people aged 16 and above in the Netherlands. Oof 3,117 LISS panel members contacted, 2,411 completed the full questionnaire (77.4% response rate), and due to combining data form earlier waves, the final sample size was 1909 respondents. Measurement of five-point attitude scales and predictor variables were included. Three scales focusing on trust in implementation of AI in radiology, robotic surgery, and dermatology were used. Level of education, immigration background, and health care utilization were included as potential predictor variables. Repeated measures Anova and Manova were used to allow comparison of the three scales and differences in different predictor variables in one analysis. All p-tests were 2-tailed and considered significant if <0.05. Findings: The overall means show that respondents have slightly more trust in AI in dermatology (M=2.90, SD = 0.73, 95% confidence interval 2.86 to 2.93) than in radiology (M=2.82, SD = 0.66, F376,66 p <0.001) and especially surgery (M=2.75, SD = 0.73 F381,74 p <0.001). The means show that males, higher educated persons, those who are employed or student, respondents with a Western background and those who were not admitted to a hospital in the past 12 months have more trust in AI than female, lower educated persons, and those with a non-Western immigration background. Trust in AI in radiology, robotic surgery and dermatology is negatively associated (r= -.610, -.594, and -556 respectively) with distrust and accountability in AI (η2 = 164) and a positively associated (r= .531, .523, and .564 respectively) with belief in efficiency of AI (η2 = 0.200). Interpretation: Unlike overall optimistic views posed in the media about AI in medicine, the general population is more distrustful of AI in medicine. The level of trust is dependent on what medical area is subject of scrutiny. Demographic characteristics and a general positive view on AI and efficiency, are significantly associated with higher levels of trust in AI. Funding: This research is part of a project funded by a grant of the Open Data Infrastructure for Social Science and Economic Innovations (ODISSEI) in the Netherlands. Declaration of Interest: None to declare. Ethical Approval: Ethical approval for the procedures in the LISS Panel was given by the board of overseers (https://www.lissdata.nl/organization/board-overseers).
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