Multidimensional trust perceptions of AI medical conversational agents - Nature

Download PDF Abstract Artificial intelligence is rapidly becoming embedded in everyday life through an expanding range of applications, services, and products. As its potential to improve diagnosis, personalize treatment, and enhance operational efficiency becomes increasingly evident, healthcare is undergoing profound transformation. However, trust and distrust operate as dual mechanisms shaping technology diffusion: trust facilitates adoption, whereas distrust constrains large-scale deployment. Trust therefore remains a persistent barrier to the widespread use of artificial intelligence in healthcare services. At present, empirical evidence on the pathways linking trust and acceptance of AI medical conversational agents (AIMCAs) remains limited. Grounded in trust theory, this study aimed to develop and validate a multidimensional trust-perception scale for AIMCAs, establish its dimensional structure and psychometric quality, and examine its associations with an external acceptance-related behavioral criterion. Methodologically, the study first used grounded-theory-informed abductive qualitative analysis to identify the structure of public trust perceptions of AIMCAs and generated and screened measurement items through expert Q-sorting; independent samples were then used for exploratory and confirmatory factor analyses, followed by assessments of internal consistency, test-retest reliability and absolute agreement, within-construct indicator convergence, discriminant validity, and criterion-related validity. Parallel analysis and the scree plot jointly supported a five-factor solution. Principal axis factoring with Direct Oblimin oblique rotation yielded a 15-item, five-dimensional structure, with the five common factors explaining 76.07% of the total variance. Confirmatory factor analysis further supported a five-dimensional structure comprising cognitive trust, affective trust, functional trust, human-like trust, and interactional trust; model-fit indices, standardized factor loadings, latent-variable correlations, and residual diagnostics collectively provided evidence for its internal structure. The Fornell-Larcker criterion and bootstrap confidence intervals for HTMT jointly provided evidence for internal discriminant validity among the five AIMCA trust dimensions. An ordinal logit model using actual use frequency as an external behavioral criterion was statistically significant overall, likelihood-ratio χ²(5) = 71.963, p < 0.001, McFadden pseudo-R² = 0.125. Interactional trust showed the strongest association with higher use frequency (OR = 3.096, 95% CI [2.257, 4.246]). The resulting scale captures multidimensional public trust perceptions of AIMCAs and provides a structured measurement basis for research on acceptance-related behavior. The findings support a 15-item, five-dimensional structure comprising cognitive trust, affective trust, functional trust, human-like trust, and interactional trust. An ordinal logit model using self-reported AIMCA use frequency as an external behavioral criterion provided additional criterion-related evidence. The scale can be used to characterize multidimensional public trust perceptions of AIMCAs and provides a structured measurement foundation for subsequent research on acceptance, use intention, continuance intention, and actual use. Explore related subjects Discover the latest articles and news in related subjects. Health care Psychology Science, technology and society Funding This study was funded by the Scientific Research Project of the Hunan Provincial Department of Education (25C0004). Author information Authors and Affiliations School of Design and Innovation, Shenzhen Technology University, Shenzhen, 518118, China Hemin Du, Wumin Ouyang & Yong Han Faculty of Innovation and Design, City University of Macau, Macau, 999078, China Hemin Du, Wumin Ouyang, Yong Han, Guanning Wang, Meng Wang & Daren Wei Waikato Management School, University of Waikato, Hamilton, New Zealand Yuyu He Authors Hemin Du View author publications Search author on: PubMed Google Scholar Wumin Ouyang View author publications Search author on: PubMed Google Scholar Yong Han View author publications Search author on: PubMed Google Scholar Yuyu He View author publications Search author on: PubMed Google Scholar Guanning Wang View author publications Search author on: PubMed Google Scholar Meng Wang View author publications Search author on: PubMed Google Scholar Daren Wei View author publications Search author on: PubMed Google Scholar Corresponding author Correspondence to Wumin Ouyang . Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Appendix Appendix See tables 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 and Fig 5 . Table 12 First-order concepts from open coding and extended raw interview evidence. Full size table Table 13 The 18 initial items of the AIMCA multidimensional trust-perception scale and their screening process. Full size table Fig. 5 Full size image Parallel analysis and scree plot for the 17 items. Table 14 CFA standardized loadings and parameter precision. Full size table Table 15 Latent-variable covariances, correlations, and standardized residual diagnostics. Full size table Table 16 Competing CFA models and modification-index diagnostics. Full size table Table 17 Measurement invariance across prior and prospective AIMCA users. Full size table Table 18 Observed covariance matrix for the 15-item CFA model in Sample 2 (n = 208). Full size table Table 19 Item-level distributional diagnostics for CFA sample 2. Full size table Table 20 Identification and independent re-estimation information for the Five-Factor CFA model. Full size table Table 21 Bifactor diagnostics for evaluating the total score in CFA sample 2 (n = 208). Full size table Rights and permissions Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . Reprints and permissions About this article Cite this article Du, H., Ouyang, W., Han, Y. et al. Multidimensional trust perceptions of AI medical conversational agents: framework development and scale validation. Sci Rep (2026). https://doi.org/10.1038/s41598-026-68299-8 Download citation Received : 04 November 2025 Accepted : 20 August 2026 Published : 29 August 2026 DOI : https://doi.org/10.1038/s41598-026-68299-8 Share this article Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy shareable link to clipboard Provided by the Springer Nature SharedIt content-sharing initiative Keywords AI medical conversational agents Multidimensional trust perceptions Abductive qualitative analysis Scale development Psychometric validation