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Cited by 14Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIHealth Education and Validation

Development, Reliability, and Structural Validity of the Scale for Knowledge, Attitude, and Practice in Ethics Implementation Among AI Researchers: Cross-Sectional Study

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Zhang, Xiaobo, Gu, Ying, Yin, Jie, Zhang, Yuejie, Jin, Cheng, Wang, Weibing, Li, Albert Martin, Wang, Yingwen +9 more·2023·JMIR Formative Research
DOI 10.2196/42202PMC PMC10636617

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Abstract

Background: Medical artificial intelligence (AI) has significantly contributed to decision support for disease screening, diagnosis, and management. With the growing number of medical AI developments and applications, incorporating ethics is considered essential to avoiding harm and ensuring broad benefits in the lifecycle of medical AI. One of the premises for effectively implementing ethics in Medical AI research necessitates researchers' comprehensive knowledge, enthusiastic attitude, and practical experience. However, there is currently a lack of an available instrument to measure these aspects. Objective: The aim of this study was to develop a comprehensive scale for measuring the knowledge, attitude, and practice of ethics implementation among medical AI researchers, and to evaluate its measurement properties. Methods: The construct of the Knowledge-Attitude-Practice in Ethics Implementation (KAP-EI) scale was based on the Knowledge-Attitude-Practice (KAP) model, and the evaluation of its measurement properties was in compliance with the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) reporting guidelines for studies on measurement instruments. The study was conducted in 2 phases. The first phase involved scale development through a systematic literature review, qualitative interviews, and item analysis based on a cross-sectional survey. The second phase involved evaluation of structural validity and reliability through another cross-sectional study. Results: The KAP-EI scale had 3 dimensions including knowledge (10 items), attitude (6 items), and practice (7 items). The Cronbach α for the whole scale reached .934. Confirmatory factor analysis showed that the goodness-of-fit indices of the scale were satisfactory (χ2/df ratio:=2.338, comparative fit index=0.949, Tucker Lewis index=0.941, root-mean-square error of approximation=0.064, and standardized root-mean-square residual=0.052). Conclusions: The results show that the scale has good reliability and structural validity; hence, it could be considered an effective instrument. This is the first instrument developed for this purpose.

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  • These limitations reinforce existing healthcare disparities, as benefits of AI solutions are predominantly confined to demographic groups already well-represented in training datasets.

    “Secondly, these limitations reinforce existing healthcare disparities, as the benefits of AI solutions are predominantly confined to demographic groups already well-represented in training datasets [37,38].”

    From Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review (2026) · cites this paper as [37]

    The Introduction discusses ethical risks of medical AI, while the Discussion’s Limitations section addresses sampling bias in this paper’s survey. Neither passage addresses healthcare disparities or whether AI benefits are concentrated among demographic groups well represented in training data. Searches for those terms and related concepts found no supporting passage in our copy. (LLM, conf=0.72)

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Authors
Zhang, X., Gu, Y., Yin, J., Zhang, Y., Jin, C., Wang, W., Li, A. M., Wang, Y., Su, L., Xu, H., Ge, X., Ye, C., Tang, L., Shen, B., Fang, J., Wang, D., & Feng, R.
Affiliations
  • Children's Hospital of Fudan University (CN)
  • Fudan University (CN)
  • Chinese University of Hong Kong (HK)
  • Shanghai Hospital Development Center (CN)
Corresponding author
Feng, Rui
ORCID
Venue
JMIR Formative Research
Publisher
JMIR Publications Inc.
Published
2023-10-26
Volume
7
Pages
e42202
ISSN
2561-326X (ISSN-L)
DOI
10.2196/42202
PMC
PMC10636617
OpenAlex
W4386991856
Cited by
14
References
10
Citations by year
2026: 7 · 2025: 7
FWCI
0.56
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Formative Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Science and Technology Commission of Shanghai Municipality (22511106001, 21511104502)
  • National Key Research and Development Program of China (2021ZD0113501)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Education › Health Education and Validation
Keywords
artificial intelligence in healthcare, AI ethics, scale development, measurement properties, KAP model, structural validity, reliability statistics

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