Abstract
Background
The field of dentistry is undergoing substantial changes due to advancements in dental AI, with its application gaining increasing attention within the profession. This study aimed to fill this gap by assessing the usage, and perceived barriers related to AI among dental students and professionals across different countries. Importantly, the focus is on educational contexts, involving both undergraduate and postgraduate dental students.
Methods
A cross-sectional multi-country study was conducted via online questionnaire between March and July 2024 to assess perceptions of AI in dentistry. Individuals under 18 years old or lacking English language proficiency were excluded. The questionnaire had three sections: demographics, knowledge and awareness of AI in dentistry, and perceptions of AI’s effectiveness in various dental conditions. Descriptive statistics and Chi-square test were done categorizing participants into undergraduate and postgraduate groups with a significance level set at p < 0.05.
Results
Half of the participants (50.2%) had prior AI experience, with a significantly higher percentage among Graduates. Graduates used AI more frequently in prosthodontics, restorative dentistry and implantology (65.7%, 56.6%, 45.8% respectively; p < 0.05). While dental students had higher AI usage in treatment planning, periodontics and orthodontics (38.7%20.4%, 19.1% respectively; p < 0.05).
Conclusions
Graduates showed higher AI usage, particularly in restorative dentistry and prosthodontics. Implementation of AI training into undergraduate and postgraduate curricula is recommended to maximize its benefits in dental education and practice.
Keywords: Artificial intelligence, Students, Usage, Knowledge, Decision making
Introduction
Artificial intelligence (AI) is a major development in the field of science and technology. In recent years, AI has significantly impacted various domains, including dentistry, by enabling technologies that simulate human cognitive functions such as learning, reasoning, problem-solving, and language comprehension [1]. AI refers to the simulation of human intelligence by computer systems, empowering them to accomplish complex tasks that usually require human cognitive effort. These systems utilize sophisticated algorithms and machine learning techniques to analyse vast datasets and support clinical decision-making [2].
In dentistry, AI has been applied to a wide range of functions, enhancing diagnostic accuracy, treatment planning, and operational efficiency. For instance, AI-assisted software can analyse radiographs and CBCT scans to identify early signs of dental caries, periodontal disease, and oral malignancies [3]. In orthodontics, AI is employed to evaluate digital dental scans, enabling the development of customized treatment plans that optimize clinical outcomes [4]. Predictive models based on AI can also estimate treatment success rates for procedures such as dental implants, root canals, and periodontal surgeries, thus improving case selection and risk management [5]. Administrative applications of AI such as appointment scheduling, electronic record-keeping, and patient follow-up contribute to enhanced workflow efficiency and higher patient satisfaction [6].
Moreover, AI supports clinical decision-making by minimizing human error and reducing manual workload, which in turn improves overall treatment quality [7, 8]. However, the high initial cost of AI system implementation limits accessibility, especially in resource-constrained dental settings. The performance of AI systems is also heavily dependent on the quality of data input; erroneous or biased data can lead to flawed diagnoses or treatment plans. Additionally, ethical concerns such as data privacy, informed consent, and autonomy in decision-making remain significant challenges in integrating AI into healthcare [9].
Recent literature has primarily emphasized the clinical benefits of AI in dentistry. Studies have demonstrated that AI algorithms can outperform traditional diagnostic methods in detecting oral diseases from radiographic images [10]. In orthodontics, AI has enhanced aligner design and treatment tracking, while in prosthodontics, it has enabled the rapid fabrication of customized prostheses with improved precision [11, 12]. Furthermore, AI tools can detect periodontal pockets and assess pulp vitality, thereby facilitating timely and accurate interventions [13].
Despite these advancements, literature lacks comprehensive studies exploring the awareness, usage patterns, and perceived barriers to AI integration particularly within the context of dental education. This gap is especially evident in developing countries, where technological adoption may lag global trends [8]. As the adoption of AI in dentistry continues to evolve, understanding the perspectives of dental students and educators is critical to inform curriculum development, teaching strategies, and future workforce preparedness.
This study aimed to fill this gap by assessing the usage, and perceived barriers related to AI among dental students and professionals across different countries. Importantly, the focus is on educational contexts, involving both undergraduate and postgraduate dental students. Hence, the findings are expected to inform curriculum planning and facilitate the strategic integration of AI into dental education. The study null hypothesis states that there is no significant difference in the usage of AI in dentistry is limited across both dental students and graduates.
Materials and methods
Study design
A cross-sectional multi-country study was conducted using an online questionnaire between March and July 2024. Ethical approval was obtained the Research Ethics Committee of the Faculty of Dentistry, Alexandria University, Egypt (#0899-03/2024) and Institutional Review Board, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia (IRB-2024-02-692).
Participants and sampling
A convenience sample of dental students, dental interns, residents, general practitioners, specialists, and academics affiliated with Egyptian or Saudi or Pakistan Dental Institutions was invited to participate in an online questionnaire. Individuals were excluded if they were under 18 years old or had insufficient English language proficiency. The required sample size was calculated based on 95% confidence level, 5% margin of error, and if 63.5% of the participating dentists were aware of AI use in dentistry to be 357 participants. However, with a final inclusion of 1027 participants, the sample size is deemed sufficient for the study.
Questionnaire
An electronic questionnaire adapted from Choudhary et al., article [14] was administered via the QuestionPro platform to evaluate the perception of individuals in the dental field regarding AI. The questionnaire was rigorously validated through a comprehensive process that included pilot testing, expert review, and statistical analysis to evaluate both construct and content validity. Furthermore, cross-cultural adaptability was addressed by adjusting the language and format to ensure clarity and accuracy in responses.
Invitations to participate in the study were disseminated through multiple channels, including email, Facebook, Twitter, and WhatsApp, targeting undergraduate and postgraduate dental students, faculty members, and general dentists. Additionally, QR codes linking to the questionnaire were displayed in lecture halls at the universities where ethical approval for the study was obtained. Thus, the participants represented the dentistry community, encompassing a diverse range of dentists with varying levels of training and expertise.
The questionnaire was preceded by an introduction that explained the purpose of the study, outlined the eligibility criteria, and reassured qualified participants that their responses would be kept confidential and anonymous as well as the participation is voluntary. Moreover, the timing of answering the questionnaire was mentioned before proceeding, which was 10 to 12 min.
The questionnaire consisted of three sections. The first section included demographic characteristics of the study sample including age, gender, nationality, education, and school of undergraduate degrees. The second section assessed familiarity and use of AI; this section evaluated whether respondents had previous experience with AI and the specific dental fields in which they have applied it, and their understanding of its advantages, such as speeding up processes and reducing errors. Additionally, it assessed participants’ knowledge of AI principles, awareness of its use in dentistry, and opinions on the most beneficial areas for AI application. The third section assessed the perception of AI among study participants including their opinions about its effectiveness as a diagnostic tool in different dental conditions, its role in clinical decision-making and treatment planning and whether AI should be incorporated into dental education at both undergraduate and postgraduate levels. Responses were measured on a 3-point Likert scale (disagree – neutral – agree).
Statistical analysis
Data was analyzed using IBM SPSS Statistics for Windows, Version 23.0 (IBM Corp., Armonk, NY), with a significance level set at p < 0.05. Descriptive statistics were presented as frequencies and percentages. Participants were categorized into dental students and graduate groups based on their education level, and their perceptions of AI were compared using the Chi-square test.
Results
Table 1 shows the demographic characteristics of the study participants. Most of the participants aged 20–30 (77%) and were females (54.8%). They were either Saudi (33.9%) or Egyptian (32.7%), with (32.9%) from Pakistan and (0.5%) from other countries. The educational background shows that (59%) were undergraduates while (41%) were post-graduates. Regarding undergraduate institutions, (41.1%) attended Middle Eastern universities, (34.1%) Saudi universities, and (23.3%) Pakistani universities.
Table 1.
Sample characteristics (n = 1027)
| Age | 18–20 | 74 (7.2%) |
| 20–30 | 791 (77%) | |
| 30–40 | 92 (9%) | |
| 40–50 | 51 (5%) | |
| 50–60 | 12 (1.2%) | |
| > 60 | 7 (0.7%) | |
| Gender | Male | 464 (45.2%) |
| Female | 563 (54.8%) | |
| Nationality | Saudi | 348 (33.9%) |
| Egyptian | 336 (32.7%) | |
| Pakistani | 338 (32.9%) | |
| Other | 5 (0.5%) | |
| Education | Undergraduate | 606 (59%) |
| Postgraduates | 421 (41%) | |
| School of undergraduate degree | Saudi university | 350 (34.1%) |
| Middle East university | 422 (41.1%) | |
| Pakistani university | 239 (23.3%) | |
| Other universities | 16 (1.6%) |
Table 2 represents the familiarity with AI among participants comparing dental students and graduates. Overall, half of the participants (50.2%) had used AI before the study, with a significantly higher percentage among (67.9%) of graduates compared to (38%) of students (p < 0.001). In terms of basic knowledge of AI’s working principles, (52%) of the total participants were familiar, with (68.9%) of graduates having significantly more knowledge than (40.3%) of the dental students (p < 0.001). Additionally, (57.8%) of participants were aware of AI usage in dentistry, with (71%) of graduates showing greater awareness compared to (48.7%) of students (p < 0.001). These differences showed a higher level of AI familiarity among graduates.
Table 2.
Familiarity of AI among participants (n = 1027)
| Familiarity of AI | Students (n = 606) |
Graduates (n = 421) |
Total (n = 1027) |
P value |
|---|---|---|---|---|
| Used AI before this study. | 230 (38%) | 286 (67.9%) | 516 (50.2%) | p < 0.001* |
| Have the basic knowledge about the working principle of AI. | 244 (40.3%) | 290 (68.9%) | 534 (52%) | p < 0.001* |
| Aware of the usage of AI in dentistry. | 295 (48.7%) | 299 (71%) | 594 (57.8%) | p < 0.001* |
*Statistically significant at p value < 0.05
Table 3 highlights the perceived advantages of AI among participants. Overall, (56.4%) of participants believed AI can deliver vast amounts of clinically relevant data in real-time, with (64.4%) of graduates agreeing significantly more than (50.8%) of students (p < 0.001). Around (34%) of both groups thought that AI had no emotional exhaustion or physical limitation, with no significant difference (p = 0.76). Additionally, (60%) agreed that AI can speed up healthcare processes and reduce medical errors, with (69.6%) of graduates showing stronger support compared to (53.3%) of dental students (p < 0.001).
Table 3.
Advantages of AI (n = 1027)
| Advantages of AI | Students (n = 606) |
Graduates (n = 421) |
Total (n = 1027) |
P value | |
|---|---|---|---|---|---|
| Advantages of using AI | AI can deliver vast amounts of clinically relevant high-quality data in real-time. | 308 (50.8%) | 271 (64.4%) | 579 (56.4%) | p < 0.001* |
| AI has no emotional exhaustion or physical limitation. | 206 (34%) | 147 (34.9%) | 353 (34.4%) | 0.76 | |
| I can speed up processes in healthcare and reduce medical errors. | 323 (53.3%) | 293 (69.6%) | 616 (60%) | p < 0.001* | |
*Statistically significant at p value < 0.05
Regarding the participants’ perceptions of AI in various aspects of dentistry among dental students and graduates. Most participants (79.9%) believed that AI will lead to major advances in dentistry, with more significant support from post-graduates compared to dental students (p < 0.001). Graduates were also more likely to view AI as a definitive diagnostic tool and useful in radiographic diagnosis of caries, periodontal diseases, soft tissue lesions and jaw pathologies (p < 0.001). In treatment planning and clinical decision making, (72.8%) and (67.3%), respectively agreed that AI can help, with significant support from graduates (p < 0.001). Additionally, (77.8%) supported AI in undergraduate training, and (80.6%) in postgraduate programs. Graduate students significantly favored its inclusion compared to dental students (p < 0.001) (Table 4).
Table 4.
Perception of AI among study participants according to education (n = 1027)
| Perception of AI | Students (n = 606) |
Graduates (n = 421) |
Total (n = 1027) |
P value | ||
|---|---|---|---|---|---|---|
| AI in Diagnosis | AI will lead to major advances in dentistry. | Disagree | 50 (8.3%) | 18 (4.3%) | 68 (6.6%) | p < 0.001* |
| Neutral | 100 (16.5%) | 38 (9%) | 138 (13.4%) | |||
| Agree | 456 (75.2%) | 365 (86.7%) | 821 (79.9%) | |||
| AI can be used as a definitive diagnostic tool in the diagnosis of diseases. | Disagree | 187 (30.9%) | 32 (7.6%) | 219 (21.3%) | p < 0.001* | |
| Neutral | 186 (30.7%) | 78 (18.5%) | 264 (25.7%) | |||
| Agree | 233 (38.4%) | 311 (73.9%) | 544 (53%) | |||
| AI could be useful in radiographic diagnosis of tooth caries. | Disagree | 69 (11.4%) | 17 (4%) | 86 (8.4%) | p < 0.001* | |
| Neutral | 148 (24.4%) | 62 (14.7%) | 210 (20.4%) | |||
| Agree | 389 (64.2%) | 342 (81.2%) | 731 (71.2%) | |||
| AI could be useful in radiographic diagnosis of periodontal diseases. | Disagree | 93 (15.3%) | 19 (4.5%) | 112 (10.9%) | p < 0.001* | |
| Neutral | 180 (29.7%) | 74 (17.6%) | 254 (24.7%) | |||
| Agree | 333 (55%) | 328 (77.9%) | 661 (64.4%) | |||
| AI can be used in the diagnosis of soft tissue lesions of the mouth. | Disagree | 116 (19.1%) | 30 (7.1%) | 146 (14.2%) | p < 0.001* | |
| Neutral | 193 (31.8%) | 91 (21.6%) | 284 (27.7%) | |||
| Agree | 297 (49%) | 300 (71.3%) | 597 (58.1%) | |||
| AI can be used in the radiographic diagnosis of pathologies in the jaws. | Disagree | 80 (13.2%) | 12 (2.9%) | 92 (9%) | p < 0.001* | |
| Neutral | 162 (26.7%) | 68 (16.2%) | 230 (22.4%) | |||
| Agree | 364 (60.1%) | 341 (81%) | 705 (68.6%) | |||
| AI in Treatment planning and Clinical decision-making | AI can be used as a treatment planning tool in diagnosis and treatment planning. | Disagree | 72 (11.9%) | 16 (3.8%) | 88 (8.6%) | p < 0.001* |
| Neutral | 131 (21.6%) | 60 (14.3%) | 191 (18.6%) | |||
| Agree | 403 (66.5%) | 345 (81.9%) | 748 (72.8%) | |||
| AI helps in clinical decision-making. | Disagree | 64 (10.6%) | 20 (4.8%) | 84 (8.2%) | p < 0.001* | |
| Neutral | 177 (29.2%) | 75 (17.8%) | 252 (24.5%) | |||
| Agree | 365 (60.2%) | 326 (77.4%) | 691 (67.3%) | |||
| AI in Education | AI applications should be part of undergraduate dental training. | Disagree | 68 (11.2%) | 23 (5.5%) | 91 (8.9%) | p < 0.001* |
| Neutral | 104 (17.2%) | 33 (7.8%) | 137 (13.3%) | |||
| Agree | 434 (71.6%) | 365 (86.7%) | 799 (77.8%) | |||
| AI applications should be part of postgraduate dental training. | Disagree | 52 (8.6%) | 10 (2.4%) | 62 (6%) | p < 0.001* | |
| Neutral | 108 (17.8%) | 29 (6.9%) | 137 (13.3%) | |||
| Agree | 446 (73.6%) | 382 (90.7%) | 828 (80.6%) | |||
*statistically significant at p value < 0.05
Figure 1 compares AI usage in various dental fields between dental students and graduates. Graduates used AI more frequently in restorative dentistry (56.6%) compared to dental students (p = 0.02) as well as in prosthodontics (65.7%) and implantology (45.8%, p < 0.001). While dental students had higher AI usage in orthodontics (19.1%) compared to 9.8% of graduates (p = 0.002) as well as in periodontics (20.4%) when compared to graduates (p < 0.001). Pedodontics had low usage overall, with 8.3% for dental students and (2.1%) for graduates (p = 0.001). In other fields, usage was comparable, with dental students (17.4%) and graduates (18.9%) with no significant difference (p = 0.66).
Fig. 1.
Field of AI use in dentistry *Statistically significant at p value < 0.05
Discussion
This study aimed to assess the usage, and barriers to artificial intelligence (AI) integration among dental students and graduates across Egypt, Saudi Arabia, and Pakistan. The results highlight an increasing level of self-reported awareness and exposure to AI technologies among graduates. These findings reflect broader global trends in AI adoption within healthcare education, where learners with greater clinical involvement and academic maturity tend to have increased interaction with advanced technologies [13, 15].
Nearly half of the study participants indicated previous experience with AI, with a significantly higher proportion among graduates. These findings suggest that graduates may have greater access to AI-driven tools through advanced coursework, clinical training, or research engagement. Thus, the null hypothesis is rejected.
Current findings come in agreement with Pringle et al., comes in agreement with this finding where most of the dentists (44%) were aware of the usage and implementation of AI in dentistry [15]. Jeong et al. [13], reported that South Korean, dental students and dental practitioners recognized AI as an important tool that can address quarries in clinical settings, though their inclination towards integrating AI varies based on professional hierarchy [13].
However, it must be emphasized that this study primarily assessed participants’ perceptions and self-reported familiarity rather than objective, knowledge-based competence in AI. The questionnaire did not test the participants’ understanding of core AI concepts such as machine learning, deep learning, or neural networks. Therefore, the responses reflect subjective awareness and usage experiences rather than technical proficiency or formal training. This distinction should be acknowledged when interpreting the findings [16].
In terms of specific applications, graduates reported higher use of AI in restorative dentistry, prosthodontics, and implantology. These specialties often involve complex treatment planning, diagnostic interpretation, and procedural simulations where AI tools are increasingly integrated [11, 17]. In addition, AI can aid in decision-making, reducing human errors and speeding up processes, especially in complex cases [18]. However, the participants in Pringle et al., (2024) reported that they were not familiar of AI usage in caries detection unlike our results [15]. Hedge et al., (2024) found disbelief among dentists in using AI’s role in decision-making [17]. The reason behind the concerns was attributed to the fact that there is limited available knowledge regarding handling challenging patient conditions and its ethical consideration.
On the other hand, dental students reported more frequent usage of AI in orthodontics, periodontics, and treatment planning. This may reflect early exposure to digital tools used for educational visualization, simulation platforms, or basic diagnostic aids that support foundational clinical skills [19].
A significant contribution of this study is its identification of perceived barriers to AI integration in dental education. Among both groups, notable barriers included a lack of formal AI-related courses in the curriculum, limited access to AI technologies within dental institutions, and insufficient hands-on experience [20]. Furthermore, some participants expressed concerns regarding the reliability of AI-generated decisions, the ethical implications of data usage, and a general hesitancy to replace human clinical judgment with algorithm-based recommendations. These findings align with previous research indicating that students remain cautious about AI practical implementation, especially in high-stakes clinical decision-making although they may view AI positively [17, 21].
The study also raises an important pedagogical question: what aspects of AI should be prioritized in dental education? While many participants supported the inclusion of AI in both undergraduate and postgraduate curricula, their responses indicate a need for nuanced content. Rather than simply advocating AI exposure; dental education programs should focus on building AI literacy. This includes training students to critically evaluate AI outputs, understand algorithmic biases, ensure patient data confidentiality, and navigate the ethical and legal dimensions of AI-assisted dentistry [20, 22].
In terms of methodology, several limitations must be acknowledged. First, although the survey was distributed to a broad sample including faculty and general dentists, the analysis was narrowed to focus solely on pregraduate and postgraduate students to preserve population uniformity and maintain alignment with the study’s educational focus. This decision was made to address the concern of heterogeneity raised in the sampling strategy, as faculty and experienced practitioners would have markedly different exposure and perspectives on AI [23].
Second, the convenience sampling method and distribution via social media platforms may have introduced self-selection bias. Individuals already interested or engaged with digital tools and AI were possibly more likely to participate, potentially inflating perceived awareness and interest levels. Third, the definition of “familiarity” with AI was not based on a validated scale or objective measurement. This limits the ability to draw strong conclusions about participants’ actual knowledge or technical skills [23].
Despite these limitations, this study provides valuable insights into the current landscape of AI awareness and readiness among dental students. The international scope and large sample size contribute to the generalizability of the findings, particularly within developing countries where digital transformation in education is ongoing [8]. The observed differences in AI usage between undergraduate and postgraduate students emphasize the importance of tailoring AI instruction to each educational level’s specific needs.
Future research should focus on developing standardized tools to assess AI literacy and competence, evaluating the impact of structured AI education interventions, and exploring faculty perspectives to create cohesive, interdisciplinary training modules. Additionally, longitudinal studies tracking the progression of AI engagement from undergraduate education through clinical practice would offer deeper insight into how AI adoption evolves over time [20, 22].
In conclusion, the study indicates a growing interest and openness toward AI among dental students, with postgraduates demonstrating higher engagement and familiarity. To ensure effective and ethical AI integration, dental education must address current barriers through structured, context-specific, and ethically grounded curriculum development.
Conclusions
This multi-country survey assessed usage, and perceived barriers to artificial intelligence (AI) among dental students and professionals. The findings revealed that graduate students demonstrated significantly higher awareness and understanding of AI principles compared to undergraduates, particularly in its application within prosthodontics, restorative dentistry, and implantology. In terms of usage, AI was more frequently employed by graduates, while dental students reported limited but notable use, especially in treatment planning, orthodontics, and periodontics. This indicates variation in AI engagement based on educational level and clinical exposure. Several barriers to adoption of AI were identified, including limited exposure, lack of formal training, and resource constraints, particularly among undergraduates. These findings suggest a need for structured strategies to address these challenges and enhance AI integration in dental education.
Acknowledgements
None.
Clinical trial number
Not Applicable.
Abbreviations
- AI
Artificial intelligence
- CBCT
Cone-Beam computed tomography
- CNN
Convolutional neural network
- IRB
Institutional review board
- SPSS
Statistical package for social sciences
Authors’ contributions
PE have made substantial contributions to the conception and design of the work; SSM did the analysis, and interpretation of data; PE, SM, SA, SSM have drafted the work and substantively revised it. All authors approved the final version before submission.
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Data availability
The datasets generated and/or analysed during the current study are not publicly available due privacy and ethical reasons but are available from the corresponding author on reasonable request.
Declarations
Ethical approval and consent to participate
was obtained from the Research Ethics Committee of the Faculty of Dentistry, Alexandria University, Egypt (#0899-03/2024) and Institutional Review Board, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia (IRB-2024-02-692). Written informed consent was obtained prior to participation for all participants. Participants were reminded of their right to withdraw anytime without consequences. Paricipants have been informed in writing that data were being made during the project and provided written informed consent for publication. All study procedures were carried out in compliance with the principles of the Declaration of Helsinki.
Consent for publication
Not Applicable.
Competing interests
The authors declare no competing interests.
Conflict of interest
The authors declare no conflicts of interest.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analysed during the current study are not publicly available due privacy and ethical reasons but are available from the corresponding author on reasonable request.