Journal Article

FZJ-2026-02830

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Positive and negative perception of artificial intelligence in healthcare: Psychometric adaptation and validation of the Turkish version

Potas, N. (Corresponding author) ; Erkılıç, C. E. ; Caner, S. ; Fritsch, S. J.FZJ*

2026
Sage London

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Please use a persistent id in citations: doi:10.1177/10519815261429412

Abstract: 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.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)
2. Center for Advanced Simulation and Analytics (CASA)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
2. SDI-S - SDI-S: Smart Data Innovation Services - Experimentelle Erprobung und Entwicklung von KI-Dienstverbünden für Innovationen auf industriellen Daten (01IS22095D) (01IS22095D)

Appears in the scientific report 2026

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Social and Behavioral Sciences ; Essential Science Indicators ; IF < 5 ; JCR ; National-Konsortium ; SCOPUS ; Social Sciences Citation Index

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