AccScience Publishing / HPR / Online First / DOI: 10.36922/hpr.0414
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RESEARCH ARTICLE

Bridging the mental health treatment gap with artificial intelligence: Public perspectives on tele-mental health services

Anas A. Alhur1* ,  Arwa A. Alhur2 ,  Saleh Altuwayrib1 ,  Saud Ayed Alshammari2 ,  Nawaf Almutairi1
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1 Department of Health Informatics, College of Public Health and Health Informatics, University of Hail, Hail, Saudi Arabia
2 Department of Psychology, College of Education, University of Hail, Hail, Saudi Arabia
HPR, 0414 https://doi.org/10.36922/hpr.0414
Received: 30 November 2025 | Revised: 5 February 2026 | Accepted: 24 February 2026 | Published online: 3 June 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Background: The incorporation of artificial intelligence (AI) into tele-mental health services has progressed rapidly in recent years; nevertheless, public acceptance of AI in psychotherapy remains ambiguous due to apprehensions regarding trust, privacy, usability, and cultural suitability. Therefore, real-world evidence is essential to understand what factors influence individuals’ willingness to adopt AI-based mental health care.

Objective: Using an extended technology acceptance model that accounts for ethical and sociocultural factors, this study examined factors influencing individuals’ adoption of AI-based tele-mental health services.

Methods: A self-administered bilingual questionnaire was used to conduct a cross-sectional online survey of 432 participants between January and June 2024. Perceived usefulness, perceived ease of use, trust and privacy, cultural fit, future readiness, familiarity with AI, and behavioral intention were all included in the proposed model. Multiple linear regression, exploratory and confirmatory factor analyses, and reliability analysis were used to analyze the data.

Results: The measurement model demonstrated satisfactory psychometric properties, including acceptable internal consistency (Cronbach’s α = 0.790–0.910), convergent validity (average variance extracted > 0.500), and discriminant validity based on the Fornell–Larcker criterion. Exploratory and confirmatory factor analyses supported a five-factor structure with good model fit (comparative fit index = 0.940; Tucker–Lewis index = 0.930; root mean square error of approximation = 0.055). Regression analysis indicated that perceived usefulness (β = 0.430, p < 0.001), trust and privacy (β = 0.270, p < 0.001), perceived ease of use (β = 0.180, p = 0.002), and cultural fit (β = 0.100, p = 0.037) were significant predictors of behavioral intention, collectively explaining 61% of the variance (R2 = 0.610). In contrast, future readiness and familiarity with AI were not statistically significant, and the corresponding hypotheses were not supported.

Conclusion: In Saudi Arabia, perceived functional value, ethical trust, usability, and cultural alignment are more important factors in the public’s acceptance of AI-based tele-mental health services than past experiences or general optimism about AI. These results highlight the significance of prioritizing data security, cultural sensitivity, and reliable system design when developing and implementing AI-based mental health services.

Keywords
Artificial intelligence
Tele-mental health
Public perception
Technology acceptance model
Perceived usefulness
Trust and privacy
Ease of use
Cultural fit
Behavioral intention
Saudi Arabia
Funding
None.
Conflict of interest
The authors declare no competing interests.
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