Perceptions and attitudes of dental practitioners toward robotic dentistry and artificial intelligence in Saudi Arabia

Robotic Dentistry (RD) and Artificial Intelligence (AI) have recently been paid great attention by dental and medical researchers due to their multiple applications.¹ AI has been considered a decision making and problem-solving model.² Moreover, convolutional neural networks learn structural patterns of a provided dataset (input) and perform tasks autonomously, resulting in a databased output.³ Functional applications of AI in dentistry include not only assisted treatment planning and computer aided diagnosis based on medical images and predictive data analytics but also clinical decision systems, which provide professional guide with computer programs. Advocators of AI have claimed that this technology facilitates and supports dental practitioners to diagnose specific oral and dental problems and to perform the treatment more effectively than human assistants and could avoid the communication gap. Diagnosis of dental problems with the AI has reached levels of human competence, changing the role of computer-assisted diagnosis from a “second opinion” tool to a more collaborative one particularly in pediatric dentistry.^4–6 

RD is a next-generation technology that opened new pathways in various areas of dentistry. Several dental robots have been developed in recent years. For example, dental training robots are realistic human-like robots called “phantoms,” while treatment robots include Simroid, nanorobots, implant robots, endomicro robots, robotic dental drill, and orthodontic arch wire bending robots.^1,7,8 Dental robots have revealed to improve precision of different dental procedures used in all departments of dentistry.^9,10 AI applications will restructure patient care, relieving dental workforce from lengthy routine tasks, improving health at lesser costs for a wider population, and ultimately assist prognostic, protective, and participatory dentistry. Evidence has demonstrated that some developing countries may struggle with heavy disease burden, untrained healthcare workers, as well as inadequate healthcare infrastructure.¹¹ 

RD and AI in dentistry have several potential benefits and challenges. To explain this further, benefits may include improving dental care quality, particularly in diagnosing of cases, cost-effectiveness, and reducing time of treatments.^12,13 Meanwhile, challenges of RD and AI may be the fact that those technologies have not considered patient–dentist interactions to attain patients’ trust, assure them, and express opinions, besides possible leakage of private information, in other words, violating patients’ privacy.^1,14–16 Yet, potential collaborations between dental practitioners, stakeholders, owners of the RD and AI factories, and policymakers may be beneficial to close the gap in understanding dentists’ perceptions and attitudes toward these technologies.

In the field of implantology and surgery, AI software has assisted plan surgeries to the minute detail before to the actual surgery. Robotic surgery is one of the greatest applications of AI in the field of surgery. AI will not replace dentists, in no ways; there exists a doubt in the supremacy of integrating AI into practice; it can never replace the role of a dentist since clinical practice is not only about diagnosing but also about correlating with clinical findings and providing personalized patient care. Although RD and AI can assist in numerous ways, a final call has to be made by a dentist as dentistry is a multidisciplinary approach.^14,17 As the field of healthcare-related AI expands, this process will certainly make an impact on young dentists in addition to those who are currently studying dentistry.¹⁴ It is becoming evident that there is a need for teaching AI technology to dental students.¹² From a dental public health perspective, it would be valuable to conduct a survey among dental practitioners with a view to evaluate their ideas and perceptions regarding the way in which the field of dentistry might be impacted by RD and AI. Therefore, the purpose of this study is to assess the perceptions and attitudes of dental practitioners in Saudi Arabia toward the use of RD and AI in dentistry.

A national dental survey was carried out using a pre-designed electronic questionnaire on a total number of 426 dental practitioners who worked in Saudi Arabia between November and December 2023. The sampling method included in this study was a simple random sampling method. The statistical demographic details of dental staff in Saudi Arabia were obtained from the Saudi General Authority for Statistics.¹⁸ A total of 17 636 dental practitioners were working in different regions of Saudi Arabia. Considering this number with a confidence level of 95% and a margin error of ±0.05, the required sample size for this study was estimated to be ∼367 dental professionals. However, to improve the participation response rate and compensate any possible refusal of participation, it was proposed to recruit more than 400 dental professionals in this study.

Data were collected by distributing an online pre-designed questionnaire through emails and text messages by the heads of Saudi dental centers to all dental practitioners working across Saudi Arabia. Informed consent was obtained from all participants before their participation in this study. After this, all participants were informed about the purpose of this study, their rights to anonymity, confidentiality, and to withdraw from this study. Then, all participants went on to fill out the online questionnaire.

A questionnaire was designed and circulated through online Google forms among dental professionals practicing in Saudi Arabia. The questionnaire was formulated, which is comprised of two parts: The first part included the questions related to the demographic information of participants, such as age, gender, years of experience, job title/rank, and qualification degree. The second part of the questionnaire is comprised of 15 multiple choice questions with a five-point Likert scale pattern that focus mainly on evaluating dental practitioners’ perceptions and attitudes toward AI and RD.

Participants valued each item in the questionnaire based on whether perceptions and attitudes were known strongly disagree, disagree, natural, agree, or strongly agree, coded as 1, 2, 3, 4, or 5, respectively. The 15 items of the questionnaire were calculated for each participant. The mean questionnaire score for each group of demographic items was also recorded. The highest achievable total score of the questionnaire is 75. Participants were then categorized into three levels based on their mean questionnaire scores: a modest level of perception/attitude (achieving a score of less than 45), a moderate level of perception/attitude (achieving a score of less than 57 but above 46), and a professional level of perception/attitude (achieving a score of more than 58).

The internal consistency of the questionnaire revealed acceptable consistency with estimated, standardized Cronbach’s α = 0.642. Table I describes the correlations between each item of the questionnaire, while a reliability analysis including the corrected item–total correlation of the questionnaire’s items and Cronbach’s alpha coefficients is illustrated in Table II.

Data were analyzed using the Statistical Package for the Social Sciences^® software (version 20.0) (IBM, Armonk, NY). Descriptive analysis was reported using the exact number of observations and related percentages, the mean, and the standard deviation (SD) to describe the data and report the sample’s diversity of this study. The t-test and analysis of variance (ANOVA) one-way test were used to determine if there is a significant statistical difference between the means of the different responses of the studied groups. Moreover, correlation analysis was also used to explore the associations between participants’ demographic characteristics and the total score of the questionnaire. The statistical significance was set at 0.05.

A total of 426 dental practitioners with a mean age of 35 ± 8.5 years participated in this study. The majority of participants were general dental practitioners (n = 276, 65%). Other practitioners were either dental specialists or consultants (n = 80, 19% and n = 70, 16%, respectively). The dental practitioners included in this study not only had different genders, nationalities, qualifications/degrees, dental specialties, and job titles/ranks, but also had different years of work experience, which highlights the diversity in the sample of this study. The summary of participants’ demographic characteristics is described in Table III.

There were 15 questions related to RD and AI in the questionnaire provided to the participants. The response to each question is described in Table IV. The overall questionnaire mean ± SD score toward RD and AI was 51.12 ± 8.2. The majority of the participants had a moderate level of perception and attitude to RD and AI, achieving questionnaire scores less than 57 but above 46 (n = 230, 54%, questionnaire mean ± SD score = 50.6 ± 3.7), while high and modest levels of perception and attitude to RD and AI were observed among the participants, achieving questionnaire scores more than 58 and less than 45, respectively (n = 102, 23.9%, questionnaire mean ± SD score = 62 ± 3.6 and n = 94, 22.1%, questionnaire mean ± SD score = 40.41 ± 2.99, respectively).

All demographic items, including gender, age, job’s title/rank, qualification/degree, and years of work experience, significantly influenced participants’ perceptions and attitudes. Younger dental practitioners obtained a significantly higher mean questionnaire score than older practitioners (p < 0.0001). Male participants achieved a significantly higher mean questionnaire score than females (52 ± 7.9 and 50 ± 8.4, respectively, p = 0.003). General dental practitioners achieved a higher mean questionnaire score than dental specialists and consultants (52 ± 8.3, 49 ± 8.3, 48 ± 6.4, p < 0.0001). Furthermore, dental staff with bachelor’s degree obtained a significantly higher mean questionnaire score than dental staff with master’s degree, Ph.D. degree, and board or equivalent degree (53 ± 8.4, 52 ± 8.8, 49 ± 6.8, and 47 ± 6.4, respectively, p < 0.0001). Moreover, it was found that participants who had less than five years of work experience achieved a significantly higher mean questionnaire score than those who had five to ten years and more than ten years of work experience (48 ± 7.9, 55 ± 7.9, and 51 ± 7.2, respectively, p < 0.0001). Table V describes the association between participants’ demographic characteristics and questionnaire mean scores. The distribution of demographic items according to the levels of participants’ perceptions/attitudes is described in Fig. 1.

RD and AI are technologies of our subsequent generations, which have opened new pathways to extend and discover the diverse regions of surgical operations. The healthcare market is a natural client of RD and AI software.¹⁹ The use of AI to support the diagnosis of diseases has been continuously expanding since the past decade.^5,6 Robotic surgery is a useful technique for performing maxillofacial surgery, which results in less blood loss and shorter hospitalization than traditional scalpel surgery.^19,20 An integration of RD and AI into implant dentistry has also drastically reshaped the field, offering unprecedented strides in oral healthcare.²¹ Moreover, robots, armed with sophisticated algorithms and sensors, undertake intricate tasks with unmatched precision, optimizing implant placement and significantly reducing errors.^20,22,23 Thus, it seems that digital dentistry has recently been moving quickly toward a revolution of RD and AI applications worldwide.

The present study showed that the majority of the participants (54%) had a moderate level of perception and attitude to RD and AI in Saudi Arabia. This is in accordance with a study in which 82.6% of the dentists had heard about RD and AI.²⁴ However, a contrasting result has been shown in other research by where most dentists were unacquainted with RD and AI.²⁵ A contrasting result has also been uncovered that 63.0% of the dental practitioners were unfamiliar with the application of AI in radiology.²⁶ The contrasting of the findings of this area of research could be attributed to the fact that different population of dental practitioners, dental interns, dental staff, and assistants besides the vision of different countries to support AI and RD may have resulted in different knowledge about the use of RD and AI.

All demographic items in the present study, including gender, age, job’s title/rank, qualification/degree, and years of work experience, significantly influenced participants’ perceptions and attitudes toward RD and AI (p = 0.003, p < 0.0001, p < 0.0001, p < 0.0001, and p < 0.0001, respectively). Interestingly, it was found that younger dentists, holding bachelor’s degree, with a licensed general dental practitioner, and less than five years of work experience obtained a significantly higher mean score than older practitioners, holding master or Ph.D. degrees, with licensed specialists or consultants, and more than five years of work experience. This may be attributed to different thoughts; for example, younger dentists may not only be more familiar with the easy use of technologies but also advocate that the availability of AI and RD technologies may be prestigious and trustful in the clinic, improved the quality of care and efficiency, facilitated their work in the future instead of traditional dental care. While older practitioners may have some opposite concerns regarding using RD and AI according to their higher work experience, including aspects such as violating patients’ privacy, security attack, importance of patient dentist interactions in diagnosing, treatment plans, providing treatment options, and delivering dental care. Moreover, with great interest, it was found in this study that male participants achieved a significantly higher mean questionnaire score toward RD and AI than females (p = 0.003). However, no study in the dental literature found that gender has significantly influenced perceptions or attitudes in relation to AI or RD. Consequently, it seems that there was a great tendency for younger dental practitioners to move toward RD and AI technologies compered to older practitioners, which may inform and change the direction of integrating RD and AI into dental practice and education in the near future.

42% of the participants in the present study strongly agreed or agreed that RD and AI will play an important role in the future of dental practice. Likewise findings in the dental literature revealed that 49% dentists strongly believed that RD and AI have a future in dentistry.²⁴ In the present study, only 30% of the participants strongly agreed or agreed that RD and AI should be a part of teaching curriculum in dentistry, unlike other research, where 74.60% and 79.80% of dental students agreed that topics about AI should be included in undergraduate and postgraduate dental education, respectively.²⁷ A further study reported that a 72.2% of medical and dental students agreed that AI and RD should be part of medical/dental training.²⁸ It could be very obvious that dental students had a great tendency toward RD and AI than dental practitioners. This may also support our previous findings where younger dentists had advocated AI and RD more than older dentists.

Most of the studies that have been conducted on the use of RD and AI have revealed that these technologies may assist the dentist in the future, but they will not be able to replace the dentists completely.^25,27 Likewise, our study showed that only 31% of the dental practitioners strongly agreed or agreed that they could be replaced by robots or AI. Furthermore, 26% of the participants in this study strongly agreed and agreed that RD and AI can increase the clinical practice of a dentist. A higher percentage in this respect was reported in the dental literature (63.7% and 61.3%).^24,25 This could be attributed to dental professionals who might think of robot as an attractive option to leave the patients in their clinic. They might also have felt that having a robot in their clinic would probably make the patients feel that the dentist is highly advanced and has latest technology in the clinic. Moreover, the present study showed that 44% of participants strongly disagreed and disagreed that RD and AI can maintain patients’ privacy. Similar results uncovered that dental and medical students and professionals felt that RD and AI can maintain patients’ privacy with main concerns raised on the expected less human interaction with the patient and any sensitive data leakage, including cybersecurity attacks (61.9% and 42.2%, respectively).²⁸ It appears that patient–dentist interaction is a key potential factor of the dental care, maintaining patients’ privacy, which may influence dentists’ thoughts and trusts toward RD and AI.

32% of respondents in the present study strongly agreed and agreed that Rd and AI could be more accurate in diagnosing dental diseases than the dentist. Other research revealed a slightly higher percentage toward accuracy of RD and AI in diagnosing dental diseases (53.4% and 60.6%, respectively).^24,25 24% of dentists in the present study strongly agreed and agreed that RD and AI could not replace teachers for clinical training and teaching in the dental schools. To the best of our knowledge, no study has addressed this particular question. However, other studies found that 59.1% of dental staff felt that RD and AI will never make the human physician expendable.²⁸ 

Digital dentistry, in general, including RD and AI may provide strengths in terms of dental care quality and cost-effectiveness and reduce time.²⁹ Meanwhile, some dental practitioners may believe that there might be still several challenges. For example, RD and AI may not take part in high-level discussions with people (patient–dentist interactions) to attain trust, assure them, and express opinions. In addition, applications of RD and AI technologies such as deep learning demands, large amounts of medical data, inadvertent leakage of private information (violating patients’ privacy), and security attack of this technology may have far-reaching negative consequences for patients, dentists, and institutions.^14,30 However, with the potential influence involving RD and AI for the future of the digital dentistry, the necessity to include these kinds of topics in the dental courses may be highlighted with suggested solutions to overcome the technology’s possible limitations. Promoting RD and AI inside dental education and curriculum, the add-on of all stakeholders inside the development process together with ensuring a legal in addition to ethical basis will be key elements to the success of RD and AI industry.

Two key limitations of this study may be highlighted: first, the generalizability of this study’s findings beyond Saudi Arabia, and, second, the use of closed-ended questions. These two limitations of the present study may produce potential biases, limit participants’ responses, and oversimplify complex questions. However, to overcome these limitations and maximize the sampling’s representation as much as possible, the recruitment was not only targeted to be more than 400 responses, but, in addition, participants included in this survey represented various demographic characteristics, emphasizing the diversity in the sample of this survey. Nevertheless, this study provides insights into the perceptions and attitudes of dental practitioners in Saudi Arabia toward the use of RD and AI in dentistry.

The overall mean and standard deviation of the questionnaire scores toward RD and AI were 51.12 ± 8.2, indicating a moderate level of participants’ perceptions and attitudes. All demographic items, including age, gender, job’s title/rank, qualification degree, and years of work experience, significantly influenced participants’ perceptions and attitudes. Promoting RD and AI inside dental education and curriculum, the add-on of all stakeholders inside the development process together with ensuring a legal and ethical basis may be considered as key elements to the success of RD and AI industry. Future research directions may include the following areas: first, exploratory qualitative approaches such as ethnographic or grounded theory to explore deep thoughts and perceptions of dentists toward RD and AI might be recommended. Second, exploration of dental education curriculum development related to RD and AI may also be advocated. Finally, potential collaborations between dental institutes and stakeholders in relation to the RD and AI industry to explore those technologies deeply and demonstrate the impacts of RD and AI models in dentistry practice might also be highlighted.

The authors have no conflicts to disclose.

Abdullah Ali H. Alzahrani: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Funding acquisition (equal); Investigation (equal); Methodology (equal); Project administration (equal); Resources (equal); Software (equal); Supervision (equal); Validation (equal); Visualization (equal); Writing – original draft (equal); Writing – review & editing (equal).

The data that support the findings of this study are available from the corresponding author upon reasonable request.