Positive and negative perception of artificial intelligence in healthcare: Psychometric adaptation and validation of...

BackgroundIn healthcare applications and organizations, perceptions of patients or the public in research play an important role in accepting artificial intelligence (AI) technologies.ObjectiveThe aims of the study were to develop: (1) to psychometrically adapt the AI in healthcare perception scale to the Turkish sample and (2) to examine statistical differences in positive and negative perceptions of AI in healthcare with the medical mistrust index, physician trust, age, and years of education, as well as the association with AI-knowledge and feelings.MethodsFor the psychometric design, the adaptation followed Beaton's cross-cultural translation stages to ensure conceptual and linguistic equivalence of the Turkish version. A correlational and cross-sectional designs were employed to examine relationships among variables. The study questionnaire was divided into four sections: a demographic form, the Perception of AI in Healthcare Scale (PAIHS), the Medical Mistrust Index (MMI), and the Trust in Physician Scale (TPS).ResultsExploratory and confirmatory factor analyses (EFA and CFA) revealed a two-factor structure representing independent positive and negative perceptions. Goodness-of-fit indices indicated an acceptable to good model fit $(p<.01)$. Cronbach's Alpha and McDonald's Omega reliability coefficients of positive-PAIH-TR were .949, .950, and negative-PAIH-TR were .956, .956. MANCOVA showed significant differences between the AI-knowledge and feelings groups according to the combination of positive and negative perception scores $(p<.01).$ConclusionsTwo independent scales were adapted and found to be psychometrically valid. Different categorizations of the scale scores were used to reveal the true nature of the neutral tendency toward AI.