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

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.

1. Introduction

Artificial Intelligence (AI) is the simulation of human intelligence processes such as thought, deep learning, engagement, and adaptation by machines, especially computer systems [1] and includes a variety of technologies that can evaluate large amounts of data such as robots, machine learning, and natural language processing [1,2]. These technologies can perform a function that usually requires human interpretation and judgment; therefore, AI has profoundly impacted various aspects of daily life, driving significant advancements across multiple domains. It can enhance efficiency and convenience through virtual assistants such as Siri and Alexa, facilitating tasks ranging from setting reminders to managing smart home devices [3]. In industrial applications, AI optimizes supply chain operations, improves customer service via chatbots, and enables sophisticated data analysis, thereby fostering innovation and operational excellence [4]. Furthermore, AI enhances accessibility for individuals with disabilities by leveraging technologies such as speech recognition and predictive text, facilitating greater independence and inclusion [5]. This pervasive integration of AI into daily life underscores its transformative potential, making routine tasks more efficient and unlocking new avenues for technological progress and societal benefit.

Healthcare and medicine are not exempt from the transformative influence of AI [1,2] and AI is currently used in a wide range of applications from diagnostic imaging to tailored treatment plans [6]. AI is also transforming dental care by significantly enhancing diagnostic accuracy, optimizing treatment plans, and improving patient outcomes [7,8,9]. AI-powered tools can analyze dental images to detect cavities, periodontal and periapical diseases, and other oral health issues with remarkable precision [10,11,12]. Machine learning algorithms can predict the progression of dental conditions and suggest personalized treatment options [13]. Furthermore, AI-driven virtual assistants can streamline administrative tasks, allowing dental professionals to dedicate more time to patient care [14]. As AI continues to evolve, its integration into dental care promises to bring about substantial advancements in both clinical practice and patient experience.

However, AI application in dentistry is influenced by patients’ opinions and attitudes, with many patients supporting the incorporation of AI in dental practices, as they believe it increases diagnosis accuracy and improves treatment results [13,15]. Nonetheless, there are concerns about personal data breaches, potential misdiagnoses, the loss of human interaction, the mistrust of AI-based dental treatments, and the potential for rising healthcare expenses [15,16]. Some scholars advocate for the implementation of proactive measures to guarantee the responsible development and deployment of AI technologies, stressing the importance of balancing innovation with the protection of patient well-being [17].

While earlier studies have primarily focused on the technical aspects of AI applications in healthcare [1], there is limited understanding of AI’s specific impact within the dental context. Notably, there is a gap in the literature regarding patient perceptions and acceptance of AI in dentistry. Therefore, this study was conducted to examine the impact of the perceived risks and benefits on patients’ willingness to accept AI in dental treatment. This research will contribute to the development of strategies that ensure the responsible and effective integration of AI in dental care, ultimately enhancing patient outcomes and satisfaction.

2. Materials and Methods

2.1. Research Model

This study examines the risks and benefits pertinent to patients in the dental context, as shown in Figure 1 and outlined below:

2.1.1. Perceived Risks

Data Privacy

This dimension is defined as the potential for unauthorized access, exposure, or misuse of sensitive data, leading to privacy violations and potential harm to individuals [16,18]. As AI systems increasingly rely on extensive personal and sensitive data to function effectively, the risks associated with data breaches have significantly escalated. Consequently, there has been an increase in discussions and debates in the literature regarding data privacy concerns [13,16,18,19,20].

Hypothesis H1:

Data privacy concerns are negatively associated with willingness to accept AI in dental treatment.

Accountability

This dimension encompasses the concern regarding the responsibility and liability for potential adverse outcomes and errors associated with AI systems. The implementation of AI technologies in dentistry raises crucial questions about accountability that need to be addressed for successful integration [15]. These issues pertain to the reliability and adoption of AI systems among users, whether they are patients or dental professionals, addressing questions such as: Who will be responsible for incorrect diagnoses provided by AI? Who will be accountable for AI system errors?

Hypothesis H2:

Accountability concerns are negatively associated with the willingness to accept AI in dental treatment.

Financial and Ethical Concerns

These are the potential adverse financial and ethical implications associated with implementing AI in dental treatment. Although AI improves diagnostic accuracy, streamlines workflows, and enhances patient outcomes, it also introduces various ethical and financial issues that require careful consideration. Implementing AI technologies in dental practices can be a significant financial investment with high initial costs of acquiring AI-powered diagnostic tools, imaging systems, and software [20]. Ongoing maintenance, upgrades, and staff training are essential investments for effectively utilizing this technology. While AI may save long-term expenses by increasing productivity and avoiding costly treatments through early detection, many dental offices may find the initial expense a significant barrier.

Hypothesis H3:

Financial and ethical concerns are negatively associated with the willingness to accept AI in dental treatment.

Communication Barriers

This is defined as the potential loss of real-life communication between patients and dentists as a result of adopting or using AI. In healthcare, effective communication between physicians and patients is crucial, as it shapes the basis of a therapeutic relationship [21], ensuring that patients completely understand their diagnosis, treatment options, and care plans, thus improving their ability to make wise choices about their health. However, the use of AI in dental treatment may cause communication issues for patients [22], as the complexity of AI systems and the technical jargon involved can be challenging for patients to understand, leading to confusion and miscommunication [23]. Also, the less personal engagement with healthcare practitioners resulting from AI technologies might lead to a sense of detachment that makes it more difficult for patients to communicate their worries about their health and receive sympathetic responses.

Hypothesis H4:

Communication barriers are negatively associated with the willingness to accept AI in dental treatment.

2.1.2. Perceived Benefits

Diagnostic and Treatment Planning Efficiency

This dimension is defined as the capability of AI to enhance diagnostic accuracy and provide more effective treatment planning to optimize care strategies. AI technologies can improve treatment planning, allowing dental professionals to develop more precise care strategies and identify optimal treatment pathways, thereby improving patient outcomes [24].

Hypothesis H5:

Diagnostic and treatment planning efficiency are positively associated with the willingness to accept AI in dental treatment.

Personalized Dental Care

This is the ability of AI to analyze patient data and provide more tailored treatment suggestions, optimizing care plans to achieve better health outcomes and patient satisfaction. AI is changing conventional dental procedures and paving the way for more effective and efficient solutions by introducing new and innovative treatment options [7]. The capacity of AI to examine enormous volumes of data makes highly personalized dental treatment possible, tailored to match specific patient needs [25]. This personalized approach improves treatment outcomes in addition to patient experience.

Hypothesis H6:

Personalized dental care is positively associated with the willingness to accept AI in dental treatment.

Patient-Enhanced Experience

AI use in dental practice or during treatment enhances the patient’s experience and satisfaction. Virtual assistants and chatbots powered by AI simplify administrative aspects such as patient record management, reminder sending, and appointment scheduling [26]. Along with shortening waiting periods, this automation ensures that patients receive quick and competent treatment. Previous studies have shown that AI and machine learning maximize patient scheduling, thereby reducing the burden on healthcare professionals and improving patient satisfaction [27]. Furthermore, AI may solve problems such as overbooking and no-shows, thus optimizing resources and raising the general effectiveness of healthcare services [28].

Hypothesis H7:

Patient enhanced experience is positively associated with the willingness to accept AI in dental treatment.

Cost Efficiency

AI has the potential to reduce the cost of dental treatments by optimizing resource allocation and streamlining administrative processes [29,30]. AI facilitates early diagnosis and timely interventions, contributing to reduced hospital stays and, consequently, lower hospitalization costs. Furthermore, the deployment of AI technology has been linked to a 15–20% decrease in readmission rates, resulting in significant cost savings [30]. Empirical evidence from diverse healthcare systems demonstrates that institutions implementing AI have realized operational cost reductions of approximately 25% [30], highlighting the transformative potential of AI in terms of enhancing the affordability and accessibility of healthcare.

Hypothesis H8:

Cost efficiency is positively associated with the willingness to accept AI in dental treatment.

2.2. Ethical Consideration

This study received approval from the Biomedical Ethics Committee at Umm Al-Qura University (approval number HAPO-02-K-012-2024-12-2365) and was carried out in compliance with the Declaration of Helsinki.

2.3. Study Setting and Subjects

The study was conducted at two public dental hospitals: the Dental Teaching Hospital and the Dental Center of Umm Al-Qura University, between September 2024 and December 2024. Both hospitals are publicly funded and government-operated, offering comprehensive dental treatments across various specialties such as endodontics, prosthodontics, oral surgery, periodontics, and orthodontics to residents and visitors of Makkah City. The participants were adults aged 18 and older undergoing dental treatment who could give informed consent and complete the questionnaire. Participants were excluded if they were unable to understand the questionnaire, could not provide informed consent, or failed to complete the questionnaire. The patients were recruited using a non-probability convenience sampling method and approached in the waiting areas and dental clinics. No incentives were offered to the respondents, and participation was entirely voluntary. The sample size was determined using the Raosoft online sample size calculator to be 586 with a 99% confidence level, a 5% margin of error, and an estimated population proportion of 50%.

2.4. Questionnaire Development and Procedure

The questionnaire comprised two sections: the first section included four demographic questions: gender, age, level of education, and whether the respondent had undergone any type of treatment using AI in dentistry; the second section included 28 items related to the perceived risks, benefits, and willingness to accept AI in dental treatment scored on a five-point Likert scale (see Supplementary Materials). Where applicable, items were adopted from prior studies and modified to align with the study context. The twelve perceived benefits of AI were stated to measure four potential factors: diagnostic and treatment efficiency, personalized dental care, enhanced patient experience, and cost and support [13,15,31,32,33]. The thirteen items measuring perceived risk were developed to assess four potential factors: data privacy, accountability, cost, and ethical concerns, as well as communication barriers [13,15,18,31,32,33,34]. The three items to measure willingness to accept AI in dental treatment were adopted from previous studies [13,35]. The original version of the questionnaire was reviewed by four faculty members, and certain items were reworded to eliminate ambiguity based on their feedback. The revised version was piloted with ten patients not included in the main study to assess its clarity, reliability, and validity, and items were modified based on their feedback. For example, the item “I am concerned about the delineation of responsibility between the AI developers, the dental professionals utilizing the technology, and the regulatory bodies overseeing its implementation” was modified to “I am worried about who will be responsible if an AI system makes a mistake in my dental treatment”. Also, the item “AI has the potential to significantly enhance my comprehensive experience during dental visits” was modified to “AI can improve my overall experience during dental visits”. Subsequently, an online version was created using Microsoft Forms and distributed to the respondents to access via a web link on a hospital tablet. They were given the option to respond either while waiting for their appointment or at the end of their visit to the dental clinic.

2.5. Data Analysis

Descriptive statistics and frequency analyses (mean, standard deviation, and percentages) were used to evaluate the distribution of the scale scores. The internal consistency of the questionnaire items within each domain was measured using Cronbach’s alpha. Hierarchical regression analysis was conducted to test the relationship between the independent variables (perceived benefits and risks) and the dependent variable (willingness to accept AI in dental treatment), controlling for variables such as gender, age, and academic year [36]. The assumptions for hierarchical regression included the normality of error terms, homoscedasticity, independence of error terms, and absence of multicollinearity among independent variables [36]. Multicollinearity was checked using variance inflation factor (VIF) analysis, with values below 10 indicating no multicollinearity issues [37]. All statistical analyses were performed using SPSS Version 29.0 (IBM Corp, Armonk, NY, USA).

3. Results

3.1. Subjects Characteristics

A total of 586 patients were invited to participate in the study, and 511 completed the questionnaire, resulting in an 87% response rate. Of the participants, 46.6% were male and 53.4% were female, with 44.4% of the participants aged under 40 years, while 55.6% were over 40 years old. Regarding educational attainment, 91.6% of the participants had a university undergraduate degree and 8.4% had postgraduate education. Additionally, 9% of the participants were undergoing AI-based treatments, while 91% had not yet utilized such treatments in their dental care.

3.2. Descriptive and Reliability Statistics

Table 1. Descriptive analysis of model dimensions and its measuring scale items.

Dimensions	Items Label	Mean (SD)	Cronbach’s Alpha (α)
Data privacy	Confidentiality	3.04 (1.13)	0.73
	Misuse	3.03 (1.12)
	Privacy	3.04 (1.13)
	Average scores	3.05 (2.72)
Accountability	Incorrect diagnoses	3.19 (1.04)	0.71
	Trust AI recommendations	3.08 (1.04)
	AI reliability	3.07 (1.04)
	Responsibility for AI errors	3.68 (1.1)
	Average scores	3.19 (1.04)
Financial and ethical concerns	High cost	3.51 (1.11)	0.60
	Inequitable access	2.97 (1.07)
	Ethical issues	2.94 (1.05)
	Average scores	3.14 (2.38)
Communication barriers	Personal interaction	3.46 (1.09)	0.60
	Preference for human treatment	3.36 (1.09)
	Communication difficulty	3.7 (1.11)
	Average scores	3.51 (2.41)
Diagnostic and treatment planning efficiency	AI-Assisted treatment planning	3.96 (0.91)	0.75
	Early Detection	3.89 (0.91)
	Reduction of human errors	3.65 (0.96)
	Average scores	3.84 (2.280)
Personalized dental care	Access to advanced treatments	3.86 (0.91)	0.77
	Innovative treatment options	3.82 (0.92)
	Personalized dental care	3.70 (0.94)
	Average scores	3.79 (2.30)
Patient-enhanced experience	Confidence in treatment	3.48 (1.02)	0.79
	Better health outcomes	3.63 (0.93)
	Improved experience	3.73 (0.91)
	Average scores	3.61 (2.41)
Cost efficiency	Cost reduction	3.28 (1.08)	0.70
	Informed decision-making	3.86 (0.90)
	Consistency and reliability	3.56 (0.9
	Average scores	3.57 (2.33)
Willingness to accept AI in dental treatment	Acceptance of AI technology	3.60 (1.03)	0.91
	Acceptance of AI diagnosis	3.56 (0.99)
	Acceptance of AI treatment plans	3.59 (1.00)
	Average scores	3.59 (2.81)

presents the descriptive statistics for each item and dimension. The highest scores among the perceived risk dimensions were for communication barriers (mean score = 3.51) and accountability concerns (mean score = 3.25), with the lowest scores awarded to data privacy concerns. In contrast, all perceived benefit dimensions had scores higher than the perceived risk dimensions, with the highest scores for diagnostics and treatment planning efficiency (mean score = 3.84) and personalized dental care (mean score = 3.79). The reliability of each dimension was ensured with an acceptable Cronbach alpha score ranging from 0.60 to 0.91.

3.3. Testing of Assumptions

Initial evaluations were conducted to confirm that the assumptions for linear multiple regression analysis were met. Initially, normality was assessed using the histogram of standardized residuals and the normal P-P plot [37]. Scatter plots demonstrated that all associations satisfied the linearity criteria. The standardized predicted values were plotted against the standardized residuals to assess homoscedasticity. The scatter plot exhibited no discernible pattern, indicating the absence of heteroscedasticity [37]. The variance inflation factor (VIF) within the advised range of 1 to 10 verified the lack of multicollinearity [37]. The independence of error terms was assessed using the Durbin-Watson statistic and was an acceptable value of 1.76.

3.4. Hypothesis Testing

Demographic variables such as gender, age, and level of education were included as control variables. All control variables in the study were dummy coded. The control variables were initially included as Model 1 in the hierarchical regression equation. The results demonstrate a nonsignificant model (F = 1.107, p < 0.346) and reveal that these variables explain 0.7% (R² = 0.007) of the variance in willingness to accept AI in dental treatment see

Table 2. Regression analysis (model summary).

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Change Statistics
					R Square Change	F Change	df1	df2	Sig. F Change
1	0.081	0.007	0.001	0.934	0.007	1.107	3	507	0.346
2	0.821	0.673	0.666	0.540	0.667	127.270	8	499	<0.001

.

Next, the eight dimensions of perceived risks and benefits were included in Model 2 to assess their contribution to predicting willingness to accept AI in dental treatment. The addition of these variables resulted in a significant overall model (F(11, 499) = 93.463, p < 0.001) and led to a 66.7% increase in the R² value (ΔR² = 0.667) (ΔF = 127.270, p < 0.001).

Initial β and t-statistic values in Model 1 suggested that there was no significant difference between male and female respondents regarding their willingness to accept AI in dental treatment (β = −0.164, t = −1.794, p = 0.073). Likewise, no significant effects were detected for age (p = 0.597) and level of education (p = 0.938); see

Table 3. Regression analysis (coefficients).

Predictor Variables	Unstandardized Coefficients		Standardized Coefficients	t	p-Value	Collinearity Statistics
	B	Std. Error	β			Tolerance	VIF
Model 1
(Constant)	3.696	0.095		39.012	<0.001
Gender	−0.164	0.091	−0.088	−1.794	0.073	0.822	1.216
Age	−0.048	0.092	−0.026	−0.530	0.597	0.827	1.210
Education level	0.012	0.149	0.003	0.077	0.938	0.993	1.007
Model 2
(Constant)	0.093	0.200		0.463	0.644
Gender	0.048	0.054	0.026	0.889	0.374	0.796	1.256
Age	−0.022	0.054	−0.012	−0.402	0.688	0.792	1.263
Education level	−0.138	0.087	−0.041	−1.584	0.114	0.978	1.023
Data privacy concerns	−0.046	0.038	−0.044	−1.211	0.226	0.491	2.038
Accountability Concerns	−0.007	0.048	−0.006	−0.145	0.885	0.434	2.302
Communication barriers	−0.030	0.038	−0.026	−0.790	0.430	0.605	1.652
Financial and ethical concerns	−0.045	0.042	−0.038	−1.063	0.288	0.512	1.952
Diagnostic and treatment planning efficiency	0.060	0.058	0.049	1.024	0.306	0.291	3.436
Personalized dental care	0.271	0.050	0.222	5.375	<0.001	0.383	2.610
Patient enhanced experience	0.548	0.050	0.471	10.951	<0.001	0.354	2.824
Cost efficiency	0.184	0.051	0.153	3.606	<0.001	0.363	2.754

Dependent Variable: Willingness to accept AI in dental treatment.

.

In Model 2, of the eight perceived risks and benefits dimensions used in the model, three had a significant effect on willingness to accept AI in dental treatment. Patient enhanced experience had a significant positive effect on willingness to accept AI in dental treatment (β = 47.1, p < 0.001), thus supporting H2. Interestingly, personalized dental care had a significant positive effect on willingness to accept AI in dental treatment (β = 22.2, p < 0.001), thus supporting H3. Additionally, cost efficiency had a significant positive effect on willingness to accept AI in dental treatment (β = 15.3, p < 0.001), supporting H4. No other significant effects were identified among the remaining dimensions, leading to the rejection of the rest of the hypotheses (see Table 3). Overall, the perceived risks and benefits accounted for 67.3% of the variance in willingness to accept AI in dental treatment (see Table 2).

4. Discussion

The present study aimed to deepen our understanding of the perceived risks and benefits associated with AI in dental treatment and how these perceptions influence patients’ willingness to accept such technology. It is anticipated that the study findings will empower stakeholders, including policymakers, dental professionals, and dental practice managers, to identify and navigate the risks and benefits associated with AI in dental treatment as perceived by patients to develop more effective strategies to address concerns, enhance patient acceptance, and ultimately improve the integration of AI technologies in dental practices.

Overall, the patients in this study perceived greater benefits and fewer risks regarding the adoption of AI in dental treatment, suggesting a generally positive attitude toward AI implementation into clinical practice. These findings are in line with the study of Ayad et al. (2023) showing that patients generally have a positive attitude toward AI in dentistry [15]. Their survey identified key perceived advantages of AI, such as enhanced diagnostic confidence, reduced treatment time, and more personalized, evidence-based care [15], implying that patients see the potential for AI to improve dental care. Three perceived benefit dimensions, namely, patient-enhanced experience, personalized dental care, and cost efficiency, significantly positively impacted patients’ willingness to accept AI in dental treatment. Interestingly, patients considered AI as the most important factor in enhancing their dental care outcomes and their visit experience, possibly because they perceived the use of AI in dental practice as a means to achieve more accurate diagnoses and personalized treatment plans. For instance, AI-driven tools can analyze dental images with high precision to identify issues that might be overlooked by the human eye. Communicating these findings to patients can significantly enhance their overall experience, improving diagnostic accuracy as well as fostering greater patient trust in the treatment process. A previous systematic review investigated the use of AI in oral radiography and its impact on the diagnosis of oral disorders [38], highlighting the significant enhancement that AI offers to diagnostic accuracy and efficiency, making it a trustworthy tool for clinical decision-making in oral diagnosis.

Younus et al. (2024) performed a comparative study assessing the efficacy of AI in dental diagnosis and treatment planning [39]. The study, which involved 500 dental patients, found that AI-driven tools significantly improved diagnostic accuracy and efficiency, with AI systems demonstrating higher sensitivity and specificity in detecting dental conditions compared to traditional methods [39]. Likewise, early detection of dental problems made possible by AI is vital in preventing the progression of significant diseases, hence lowering the need for involved complex and invasive dental procedures [40,41]. Turosz et al. (2024) also demonstrated that AI algorithms achieved high accuracy in detecting dental issues such as missing teeth and root canal fillings, with performance metrics exceeding 90% [42]. This supports that AI can significantly enhance diagnostic precision in dental practice. Additionally, Pauwels (2021) highlighted the potential of AI in dental imaging, emphasizing its ability to process complex imaging data with high accuracy through deep learning models [43]. Thus, AI not only improves diagnostic accuracy but also enhances patient trust by providing clear and precise diagnostic information.

In our study, personalized dental care also positively affected patients’ willingness to accept dental treatment. For instance, AI-driven tools can provide highly accurate diagnoses and tailored treatment plans, offering more personalized and evidence-based disease management [15]. Additionally, Al-Dabbagh et al. (2024) revealed that while most patients were aware of AI in dentistry, many had not experienced AI-based treatments and expressed mixed feelings about their use. Despite this, the study highlighted that patients who had encountered AI in dental practice appreciated its potential for enhancing diagnostic accuracy and personalized care [13]. These findings suggest that while there is some skepticism, the perceived benefits of AI in providing personalized dental care can positively influence patients’ willingness to accept AI-driven treatments. AI models, like those used in orthodontic treatment planning, can accurately predict how patients will be treated, providing them with more options and better outcomes [44]. Similarly, an innovative hybrid framework that integrated a deep learning architecture with conventional computer-aided diagnosis processing enabled the automatic detection and classification of periodontal bone loss on dental panoramic radiographs [45], significantly enhancing overall treatment and improving patient experience.

Although there was a negative relationship between perceived risk dimensions and willingness to accept AI in dental treatment, the respondents did not report any significant effect of risks on their willingness to accept dental treatment. This differs from the existing body of literature, which highlights the potential risks of AI and its impact on its use in dentistry [15,17,31,46]. It is possible that advancements in AI technology and its growing presence in daily life may have contributed to a more favorable perception of AI among participants. As AI becomes more widespread and its benefits better understood, patients may be less worried about potential risks and more inclined to focus on the advantages it provides. Additionally, patients may have greater awareness and understanding of AI and its applications, which can further reduce their perceived risks.

Besides the risks and benefits associated with AI, most participants admitted that they have not tried or experienced any dental treatment using AI. This lack of firsthand experience could be attributed to many dental practices, particularly public clinics, not yet fully integrating AI technologies. The slow adoption of AI in public dental practices may be due to various factors, including limited funding, inadequate infrastructure, and the need for extensive training for dental professionals [7]. Also, concerns regarding the economic ramifications of AI in their dental care are abundant among patients. Increased treatment costs resulting from the expensive initial investment and continuous expenditures for AI systems might be passed on to patients [17], making dental care less affordable for some and widening the gap in access to high-quality treatments. Patients are questioning whether the additional costs will be justified by the benefits of AI and if these technologies will truly lead to better outcomes for them. A previous study found that rising dental care costs were ranked as one of the top three perceived disadvantages of AI in dentistry [15].

Apart from the expenses, people have moral questions about the use of AI in their dental treatment, mainly due to the transparency of AI decision-making procedures and uncertainty about whether these systems introduce biases that could compromise the standard of treatment they receive [47,48]. Consequently, patients may not have the opportunity to experience the potential benefits of AI-driven dental treatments firsthand. Increasing the adoption of AI in dental practices, along with educational initiatives to inform patients about its advantages, could help bridge this gap.

Limitations and Future Research Directions

This study has some limitations that should be acknowledged. Since the sample only included those seeking dental treatment in two public dental hospitals in Saudi Arabia, the generalizability of the results may be limited, as the findings may not fully represent the broader population or patients from different regions and types of dental practices. Future research should consider including larger and more diverse samples from multiple dental hospitals and clinics across various regions to validate the findings and provide a more comprehensive understanding of patients’ perceptions and acceptance of AI in dental treatment.

This study relied on quantitative data and, thus, lacked detailed qualitative insights into patients’ perceptions of AI usage in dentistry. Future research adopting a qualitative approach could employ methods such as interviews, focus groups, and open-ended surveys to explore these perceptions in depth. By integrating qualitative insights, future research can offer a more comprehensive view of the acceptance and concerns related to AI in dental care. Although the risk and benefit dimensions in this study explained 67.3% of the variance, which is considered acceptable, these dimensions were derived from a literature review and expert opinions on their relevance and importance in a dental context. Given the rapid and continuous advancements in AI, some dimensions may have been overlooked in this study, and new, more pertinent dimensions may emerge as AI usage in dentistry evolves. Future research should explore additional risk and benefit dimensions to better measure AI acceptance. Finally, longitudinal studies could be conducted to assess changes in patients’ perceptions and acceptance of AI over time, especially as AI technologies continue to evolve and become more integrated into dental practices to offer valuable information on the long-term effects and sustainability of AI acceptance in dentistry.

5. Conclusions

The perceived benefits of AI in dental treatments, such as improved accuracy, efficiency, and better patient outcomes, significantly positively impacted patients’ acceptance of AI technologies, highlighting the positive aspects of AI in terms of achieving patient acceptance and facilitating the successful integration of AI technologies in dental practices, potentially revolutionizing the future of dental care.