Predicting Artificial Intelligence Acceptance in Dental Treatments Among Patients in Saudi Arabia: A Perceived Risks and Benefits Perspective

Background/Objectives: Artificial Intelligence (AI) is transforming dentistry by offering advanced solutions to improve diagnostic accuracy, optimize treatment planning, and advance patient care. However, as AI becomes more prevalent in dental practice, patients may have concerns and skepticism about its implementation. Therefore, this study aims to explore the impact of the perceived risks and benefits on patients’ willingness to accept AI in dental treatment. Methods: This cross-sectional study was conducted in two public dental hospitals, and 586 patients were invited to complete a 28-item questionnaire. In total, 511 questionnaires were completed, resulting in a response rate of 87%. Multiple regression analysis was performed to assess the impact of perceived risks and benefits on patients’ willingness to accept AI in dental treatment. Results: All dimensions of perceived benefits had higher mean scores compared to the perceived risks. Additionally, three perceived benefit dimensions had a significant positive influence on the willingness to accept AI: patient-enhanced experience (β = 47.1, p < 0.001), personalized dental care (β = 22.2, p < 0.001), and cost efficiency (β = 15.3, p < 0.001). Conclusions: The perceived risks had little impact on patients’ willingness to accept AI, suggesting patients may be unaware of or unconcerned about AI’s potential risks in dentistry. Future research should investigate these perceptions and other dimensions influencing AI acceptance.

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Publication Details

Journal
Oral
Published
2025-04-16
DOI
https://doi.org/10.3390/oral5020028
Citations
3
Primary Topic
Dental Research and COVID-19
Type
article
Field-Weighted Citation Impact
8.47
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article

Predicting Artificial Intelligence Acceptance in Dental Treatments Among Patients in Saudi Arabia: A Perceived Risks and Benefits Perspective

Yousef Ezzat, Rayan M. Sharka, Mohammed Alamar, Manal Alshareef et al.
3 citations
Oral
Dental Research and COVID-19
8.47
article

Predicting Artificial Intelligence Acceptance in Dental Treatments Among Patients in Saudi Arabia: A Perceived Risks and Benefits Perspective

Yousef Ezzat, Rayan M. Sharka, Mohammed Alamar, Manal Alshareef, Bayan Skatawi, Ghaday Sayyam, Maya Abutaleb, Lujain A Abualkhair
article en
3 citations

Abstract

Background/Objectives: Artificial Intelligence (AI) is transforming dentistry by offering advanced solutions to improve diagnostic accuracy, optimize treatment planning, and advance patient care. However, as AI becomes more prevalent in dental practice, patients may have concerns and skepticism about its implementation. Therefore, this study aims to explore the impact of the perceived risks and benefits on patients’ willingness to accept AI in dental treatment. Methods: This cross-sectional study was conducted in two public dental hospitals, and 586 patients were invited to complete a 28-item questionnaire. In total, 511 questionnaires were completed, resulting in a response rate of 87%. Multiple regression analysis was performed to assess the impact of perceived risks and benefits on patients’ willingness to accept AI in dental treatment. Results: All dimensions of perceived benefits had higher mean scores compared to the perceived risks. Additionally, three perceived benefit dimensions had a significant positive influence on the willingness to accept AI: patient-enhanced experience (β = 47.1, p < 0.001), personalized dental care (β = 22.2, p < 0.001), and cost efficiency (β = 15.3, p < 0.001). Conclusions: The perceived risks had little impact on patients’ willingness to accept AI, suggesting patients may be unaware of or unconcerned about AI’s potential risks in dentistry. Future research should investigate these perceptions and other dimensions influencing AI acceptance.

OralVol. 5(2)
Yanbu University College (SA), Umm al-Qura University (SA), Ministry of Health (SA), Royal Commission Medical Center (SA)
Openalex Percentile: Top 3%
Dental Research and COVID-19
8.47
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