Currently submitted to: Journal of Medical Internet Research
Date Submitted: Jul 27, 2026
Open Peer Review Period: Jul 28, 2026 - Sep 22, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Artificial Intelligence Attitude Measurement Instruments in Healthcare: A Systematic Review of Measurement Properties
ABSTRACT
Background:
The rapid advancement of artificial intelligence (AI) in healthcare has led to the development of numerous instruments for assessing attitudes toward AI. Although a variety of instruments have been developed for healthcare populations, the quality of their measurement properties, the certainty of the supporting evidence, and their applicability have not been systematically evaluated.
Objective:
Systematically evaluate the measurement properties and methodological quality of AI attitude measurement instruments in the medical field, and to provide evidence-based recommendations for healthcare administrators in selecting appropriate measurement instruments.
Methods:
A systematic search was conducted in PubMed, Embase, Web of Science, and CINAHL databases to identify studies assessing attitudes toward AI among healthcare populations. The search covered all records from database inception to January 28, 2026. The methodological quality and measurement properties of included instruments were assessed following the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines. The quality of each instrument was rated, and overall recommendations were formulated.
Results:
A total of 30 studies involving 17 artificial intelligence attitude measurement instruments were included. Most instruments demonstrated satisfactory structural validity and internal consistency; however, evidence regarding content validity, cross-cultural validity, and criterion validity remained limited. Hypothesis testing for construct validity showed generally favorable results. Based on the overall assessment of measurement properties and evidence grading, nine instruments were classified as A-level recommendations, six as B-level recommendations, and two as C-level recommendations.
Conclusions:
AAAW demonstrated the most favorable overall measurement properties among existing artificial intelligence attitude measurement instruments in the medical field and is recommended for current use. However, the overall methodological quality of available instruments remains limited due to insufficient reporting of measurement properties and methodological procedures, as well as heterogeneity among target populations. Future studies should adhere to standardized instrument development guidelines, enhance methodological rigor and generalizability, and promote the development of reliable measurement instruments to support the evidence-based implementation of artificial intelligence in healthcare. Clinical Trial: PROSPERO CRD420261365231ï¼›https://www.crd.york.ac.uk/PROSPERO/recorddashboard
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