Abstract
Introduction
Digitalization, which is rapidly increasing its influence across many fields, has also led to a significant transformation in dentistry, evolving into a fundamental component that dentists are expected to master alongside traditional practices. Within this transformation in digital dentistry, teledentistry and artificial intelligence applications have emerged as prominent areas of focus. This study aimed to evaluate dental students’ knowledge, attitudes, and perceptions regarding teledentistry and artificial intelligence-supported systems. It also investigated their views on the future applications of these technologies and compared the awareness and expectations of preclinical and clinical students.
Methods
This descriptive cross-sectional study was conducted among dentistry students at the Faculty of Dentistry, Sakarya University, including both preclinical and clinical levels. Data were collected using an 18-item structured questionnaire developed by the researchers and administered online via Google Forms. The internal consistency of the questionnaire was assessed using Cronbach’s Alpha, and multiple linear regression analysis was performed to identify predictors of teledentistry knowledge levels. Statistical analyses were performed using IBM SPSS Statistics for Windows, Version 22.0. Descriptive statistics (frequencies and percentages) were calculated, and Chi-square tests were used to compare categorical variables. A p-value of < 0.05 was considered statistically significant.
Results
A total of 368 students participated in the study, of whom 237 were female and 131 were male; 156 were preclinical and 212 were clinical students. Clinical students demonstrated significantly higher knowledge regarding the purpose of artificial intelligence-based system usage in teledentistry compared with preclinical students (p < 0.05). Additionally, a significant difference was found in the sources of information about teledentistry between the two groups (p < 0.05). Social media was the most common information source for both groups, while a higher proportion of preclinical students reported having no prior knowledge of teledentistry (64.7%). Most participants identified artificial intelligence and big data analytics as the most influential technologies for the future development of teledentistry.
Conclusions
Integrating teledentistry and artificial intelligence more extensively into undergraduate dental education may enhance future dentists’ competence in digital dentistry and better prepare them to adapt to rapidly evolving technological advancements in the profession. The findings also indicate that dental students generally demonstrate positive attitudes toward AI-supported teledentistry despite having limited knowledge levels, with clinical students showing higher levels of awareness compared with preclinical students.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s12909-026-09878-1.
Keywords: Teledentistry, Dental student, Artificial intelligence
Introduction
Today, the rapid digitalization of healthcare creates numerous opportunities for medical professionals to incorporate advanced technologies into their clinical practices. This transformation also empowers patients by providing easier access to information regarding their health and treatment options [1, 2]. Within this technological evolution, various applications have emerged, commonly referred to as “telemedicine” or “eHealth.” In the specific context of dental care, these applications are known as “teledentistry” [2]. Teledentistry leverages digital tools—including interactive audio, video, and data transmission technologies—to facilitate communication and information exchange in dental healthcare [3]. By enabling remote dental consultations, education, and treatment, teledentistry reduces the dependency on direct face-to-face interactions between dental practitioners and patients, thereby expanding access to care and improving efficiency in service delivery [4].
The term teledentistry was first introduced by Cook in 1997, defined as the use of video conferencing technologies to remotely diagnose patients and provide treatment advice [5]. Today, with the integration of artificial intelligence (AI) technologies, teledentistry is rapidly evolving and expanding into new dimensions. AI, as a scientific field focused on developing intelligent systems and machines that mimic human cognition through pre-programmed algorithms, offers effective support to dental professionals, researchers, and academics in remote diagnosis, treatment planning, and clinical decision-making processes [6]. These innovative technologies are broadening the scope of teledentistry, enabling faster, more accurate, and more accessible dental care solutions. Additionally, a study has indicated that teledentistry can serve as a cost-effective approach, helping to reduce overall healthcare expenses while maintaining quality care [7].
Although telemedical applications are widely used in healthcare, many dentists may still have limited awareness of teledentistry’s nature, its potential benefits, and its integration into routine practice [8]. This gap in awareness also highlights the need to better understand how teledentistry is perceived within the dental profession, particularly among future practitioners.
While previous studies have assessed dentists’ knowledge and attitudes toward teledentistry [9–11], research specifically targeting dental students remains limited in the existing literature. In particular, studies comparing preclinical and clinical students regarding awareness and knowledge of artificial intelligence use in teledentistry applications are limited. Assessing the awareness and knowledge levels of dental students—who will be the future practitioners of digital health technologies—is of great importance. In this context, the present study aimed to evaluate dental students’ knowledge, attitudes, and awareness regarding teledentistry and the use of artificial intelligence in teledentistry applications. Furthermore, it evaluates students’ opinions regarding the potential future applications of these technologies and compares preclinical and clinical students to identify differences in awareness, knowledge levels, and expectations. The findings are expected to reveal the current state of knowledge in this field and provide valuable insights for developing educational strategies that promote digital and AI literacy in dental education. It was hypothesized that clinical dental students would demonstrate higher levels of knowledge and awareness regarding teledentistry and AI applications in teledentistry compared with preclinical students.
Methods
This descriptive cross-sectional study was conducted among preclinical and clinical dentistry students at the Faculty of Dentistry, Sakarya University. Students who had not yet started direct patient care were classified as preclinical students, whereas students actively involved in clinical training and patient care were classified as clinical students. Participants were recruited using a convenience sampling approach based on voluntary participation. Data were collected using a structured questionnaire consisting of 18 items, developed by the researchers and administered online via the Google Forms platform. The survey was adapted from previous studies [12–14]. The questionnaire was originally prepared and administered in Turkish. For manuscript preparation purposes, the questionnaire items were subsequently translated into English by the researchers. The questionnaire used in this study was developed by the authors and is provided as a supplementary file in English.
All students enrolled in the Faculty of Dentistry were invited to participate voluntarily. Students who did not provide consent or submitted incomplete questionnaires were excluded from the analysis. The questionnaire items were developed based on the relevant literature and refined with expert feedback from two faculty members specializing in dental public health (B.B.Ş. and M.B.) to ensure content validity. This study was approved by the Sakarya University Faculty of Medicine Scientific Research Ethics Committee (Approval date: November 18,2025, Decision no: 605, Document No: E-43012747-050.04-533683). The study was conducted in accordance with the principles of the Declaration of Helsinki.
The target population for this census-based study included all undergraduate dental students. An a priori power analysis was conducted (G*Power 3.1.9.7) to determine the minimum sample size required for robust Chi-square comparisons. With a medium effect size (w = 0.3), α = 0.05, and a power of 0.95, the minimum requirement was calculated as 145 participants per analysis. The study aimed to reach all eligible students to maximize representativeness and minimize selection bias.
The first two questions aimed to obtain demographic information of the participants. Questions 3–8 were designed to assess students’ knowledge regarding teledentistry, while questions 9–18 focused on evaluating their perceptions of teledentistry and opinions about its future. The questionnaire included both multiple-choice and multiple-response formats.
For the knowledge-based questions (Q3–Q8), responses were classified as “correct” or “incorrect” according to current literature, ADA teledentistry guidance documents, and previously published studies related to teledentistry and AI applications in teledentistry [13–15]. To obtain a continuous dependent variable suitable for multiple linear regression, a scoring system was established for the knowledge-based questions (Questions 3–8). Each correct response in multiple-choice and multiple-response formats was assigned ‘1’ point, while incorrect or ‘don’t know’ responses were assigned ‘0’ points, resulting in a cumulative ‘Total Knowledge Score’. Nominal variables that do not allow for hierarchical scoring, such as sources of information, were excluded from this cumulative score to maintain construct validity.
The internal consistency of the teledentistry knowledge questionnaire was evaluated using Cronbach’s Alpha, yielding a coefficient of α = 0.727. Since a Cronbach’s Alpha value of 0.70 or higher is considered sufficient for the reliability of a measurement instrument, the survey was considered reliable. Additionally, Corrected Item-Total Correlations were examined to assess the contribution of each item to the overall scale. Even though some items showed lower correlation coefficients, they were retained in the analysis due to their clinical relevance and specific importance to the domain of teledentistry.
Data were analyzed using IBM SPSS Statistics for Windows, Version 22.0. Descriptive statistics, frequency, and percentage distributions were calculated. For multiple-response questions, data were analyzed using multiple response sets, and results were presented as frequencies and percentages of responses and cases. Chi-square tests were applied to compare categorical variables. The strength of the association between variables was assessed using effect size coefficients. Phi (φ) was employed for 2*2 contingency tables, while Cramer’s V was used for larger tables. The effect sizes were interpreted according to standard conventions: 0.10 as small, 0.30 as medium, and 0.50 as large effect. Furthermore, Multiple Linear Regression analysis was performed to identify independent predictors (grade and gender) of teledentistry knowledge levels. The assumption of normality was verified using P-P plots of the residuals. Multicollinearity was assessed using Variance Inflation Factor (VIF) values. A p-value of < 0.05 was considered statistically significant.
Results
The demographic data of the participants are shown in Table 1. A total of 368 students completed the survey. The sample consisted of 237 (64.4%) female and 131 male (35.6%); 156 (42.4%) preclinical and 212 (57.6%) clinical students. Although a numerical discrepancy exists between the two groups due to the voluntary nature of participation and varying class sizes, both subgroups independently exceeded the minimum power threshold of 145 established in the methodology.
Table 1.
Distribution of participants by gender and educational stage
| Gender | Preclinical (n) | Clinical (n) | Total (n) |
|---|---|---|---|
| Female | 102 (65.4%) | 135 (63.7%) | 237 (64.4%) |
| Male | 54 (34.6%) | 77 (36.3%) | 131 (35.6%) |
| Total | 156 (42.4%) | 212 (57.6%) | 368 (100%) |
Table 2 presents the comparison of preclinical and clinical students’ knowledge about teledentistry. Among the items assessing general knowledge, a statistically significant difference was observed only in the question related to the purpose of AI-based systems (Q4) (p < 0.05). Clinical students demonstrated a higher level of awareness in this area, and the Cramer’s V value of 0.12 confirms that this difference is based on a small but statistically verified relationship.
Table 2.
Assessment of the groups’ knowledge about teledentistry
| Questions | Preclinical | Clinical | p-value | Effect Size (V/φ) | ||
|---|---|---|---|---|---|---|
| Correct | Incorrect | Correct | Incorrect | |||
| Q3. What is teledentistry? | 101 (64.7%) | 55 (35.3%) | 136 (64.2%) | 76 (35.8%) | 0.9 | 0.07 V |
| Q4. For what purpose are AI-based systems used in teledentistry? | 106 (67.9%) | 50 (32.1%) | 168 (79.2%) | 44 (20.8%) | 0.01* | 0.12 V |
| Q5. Is teledentistry a supportive tool for current dental services? | 141 (90.4%) | 15 (9.6%) | 183 (86.3%) | 29 (13.7%) | 0.2 | 0.06 φ |
* Indicates statistically significant difference (p < 0.05)
Conversely, no significant differences were found between the groups in defining teledentistry (Q3) or in recognizing it as a supportive tool for current dental services (Q5) (p > 0.05). The low effect size values for these questions (Cramer’s V = 0.07 for Q3 and Phi = 0.06 for Q5) confirm that the students’ knowledge levels in these categories are statistically similar across both groups, reflecting a weak association between the level of education and these specific knowledge domains.
When the sources from which participants obtained information about teledentistry (Q6) were analyzed, a statistically significant difference was observed between the groups (p < 0.05). Social media was identified as the most common source of information for both groups, and a higher proportion of preclinical students reported having no prior knowledge of teledentistry (64.7%). The Cramer’s V value of 0.23 confirms that the difference in information sources between education levels is based on a notable and statistically verified relationship.
No significant difference was found between the groups in professional use intentions (Q7) (p > 0.05), where the most frequent response was “Maybe” in both groups. The low effect size (Cramer’s V = 0.10) confirms that professional intentions are statistically similar regardless of the students’ clinical stage.
A statistically significant difference was found in perceptions of the dentist–patient relationship (Q8) (p < 0.05). A greater proportion of clinical students believed that teledentistry would strengthen this relationship, whereas some preclinical students stated that it might negatively affect communication. The Cramer’s V value of 0.19 confirms that clinical experience has a small to moderate but statistically verified impact on how students perceive the future of patient-doctor interaction.
No statistically significant differences were found between the groups regarding the potential replacement of traditional practices (Q9) or the necessity of compulsory education (Q10) (p > 0.05). The very low effect sizes (Cramer’s V = 0.11 for Q9 and 0.05 for Q10) further confirm the strong statistical similarity between the groups, as most participants collectively agreed that teledentistry cannot replace traditional practices and that training should be specialty-specific.
Regarding the cost of services (Q11), most participants indicated that teledentistry should be free or free for certain patient groups. The distribution of responses was similar between clinical and preclinical students (p > 0.05), with an effect size of 0.18 (Cramer’s V), reflecting a relatively weak association between educational level and cost-related perceptions (Table 3).
Table 3.
Students’ opinions on teledentistry
| Question & Responses | Preclinical n (%) | Clinical n (%) | p-value | Effect Size (V) |
|---|---|---|---|---|
| Q6. Where did you get information about teledentistry? | 0.001* | 0.23 | ||
| University undergraduate courses and seminars | 15 (9.6) | 33 (15.6) | ||
| Courses and seminars organized by other institutions | 4 (2.6) | 14 (6.6) | ||
| Websites of professional organizations (e.g., WHO) | 3 (1.9) | 7 (3.3) | ||
| Published scientific articles | 5 (3.2) | 3 (1.4) | ||
| Social media | 28 (17.9) | 64 (30.2) | ||
| I have no knowledge | 101 (64.7) | 91 (42.9) | ||
| Q7. Would you consider using teledentistry in your future career? | 0.12 | 0.10 | ||
| Definitely yes | 34 (21.8) | 51 (24.1) | ||
| Maybe | 104 (66.7) | 149 (70.3) | ||
| No | 18 (11.5) | 12 (5.7) | ||
| Q8. How do you think teledentistry affects the patient–doctor relationship? | 0.003* | 0.19 | ||
| Makes it more effective | 45 (28.8) | 83 (39.2) | ||
| May negatively affect it due to reduced face-to-face communication | 56 (35.9) | 42 (19.8) | ||
| Has no effect | 3 (1.9) | 10 (4.7) | ||
| Increases patient access to the dentist | 52 (33.3) | 77 (36.3) | ||
| Q9. Can teledentistry replace traditional dentistry? | 0.09 | 0.11 | ||
| Yes | 13 (8.3) | 18 (8.5) | ||
| No | 87 (55.8) | 140 (66.0) | ||
| Partially | 56 (35.9) | 54 (25.5) | ||
| Q10. Should teledentistry training be mandatory for dentists? | 0.50 | 0.05 | ||
| Yes | 41 (26.3) | 57 (26.9) | ||
| No | 30 (19.2) | 50 (23.6) | ||
| Only for certain specialties | 85 (54.5) | 105 (49.5) | ||
| Q11. Should teledentistry services be free of charge? | 0.70 | 0.04 | ||
| Yes | 70 (44.9) | 102 (48.1) | ||
| No | 20 (12.8) | 28 (13.2) | ||
| Free only for certain patient groups | 66 (42.3) | 82 (38.7) |
Percentages may not total 100% due to rounding
* Indicates statistically significant difference (p < 0.05)
Figures 1 and 2 present the multiple-choice questions and the percentage distributions of responses among preclinical and clinical dental students. Video conferencing and teleconsultation were the most frequently identified teledentistry technologies, while preventive/public dental health and dental diagnosis were the most commonly selected application areas. Participants most frequently reported the need for further development of AI-assisted diagnostic systems and identified technological infrastructure, data privacy concerns, and insufficient professional knowledge as the main barriers to implementation. Increasing patient awareness and professional training were among the most frequently selected strategies, whereas artificial intelligence and big data analytics received the highest proportion of responses regarding future technologies.
Fig. 1.
Responses to Q12–Q15. Note: All questions were multiple-response questions; therefore, percentages may exceed 100%. Percentages were calculated based on the total number of participants in each group
Fig. 2.
Responses to Q16–Q18. Note: All questions were multiple-response questions; therefore, percentages may exceed 100%. Percentages were calculated based on the total number of participants in each group
The multiple linear regression model was statistically significant (F = 5.325, p = 0.005), and the VIF values for all variables were 1.000, confirming the absence of multicollinearity. After controlling for gender, clinical level was identified as a significant and independent predictor of the cumulative knowledge score (p = 0.002). This indicates that the academic stage of the students significantly influences their comprehensive knowledge regarding teledentistry, independent of other demographic factors (Table 4).
Table 4.
Multiple linear regression analysis of factors affecting teledentistry knowledge levels
| Independent Variables | Unstandardized Coefficients | Standard Error | Standardized Coefficients Beta |
t | P | VIF |
|---|---|---|---|---|---|---|
| (Constant) | 54.723 | 1.517 | - | 36.063 | 0.000 | - |
| Gender | 0.668 | 0.729 | 0.047 | 0.916 | 0.36 | 1.000 |
| Clinical Level | 2.225 | 0.707 | 0.162 | 3.148 | 0.002* | 1.000 |
Dependent Variable: Cumulative Knowledge Score. R² = 0.028; Model Significance: F = 5.325, p = 0.005
* Indicates statistically significant difference (p < 0.05)
Discussion
In recent years, digitalisation and AI applications have rapidly become part of everyday life, and in parallel, digital transformation in healthcare services has accelerated. The literature reports that teledentistry may serve as an effective and efficient alternative to face-to-face oral and dental healthcare delivery [16]. In addition, teledentistry has been shown to facilitate patient care, prepare dental students for clinical practice, and support the delivery of effective services to a wider patient population [17]. In line with these developments, the increasing use of remote healthcare applications has made it necessary to assess the knowledge and readiness of dental students—future dental professionals—regarding teledentistry and related digital technologies. Although several studies have examined dental students’ knowledge and perspectives on teledentistry, no study to date has specifically investigated the role of AI in teledentistry.
In this study, a questionnaire-based survey was conducted to evaluate the knowledge levels and perspectives of preclinical and clinical dental students regarding teledentistry and the use of AI in teledentistry. In addition to assessing students’ knowledge and attitudes, differences between preclinical and clinical students with respect to these parameters were also examined.
Previous studies have reported that teledentistry enables teleconsultations through internet-based platforms without time or location constraints and can reduce clinical workload during crisis periods by facilitating safer patient counselling [18]. However, despite the demonstrated benefits of teledentistry and advances in AI, teledentistry programmes have been implemented at the national level in only a limited number of countries [19]. Al Assad et al. [20] reported that awareness of teledentistry in the field of dentistry was generally at an acceptable level; however, knowledge and skill levels related to this practice were insufficient (68.6%). In the present study, 64.4% of students demonstrated correct knowledge regarding teledentistry. This minor difference may be explained by differences in sample characteristics between the two studies. When knowledge levels regarding the purposes of AI use in teledentistry were examined, clinical students (79.2%) were found to have significantly higher knowledge levels than preclinical students (67.9%) (p < 0.05). Furthermore, multivariate regression analysis demonstrated that clinical level independently predicted higher cumulative teledentistry knowledge scores, even after controlling for gender. Similar findings have been reported in previous studies, which demonstrated that students with greater clinical exposure and educational experience exhibited higher levels of knowledge and more positive attitudes toward teledentistry [13, 21]. This finding suggests that clinical exposure may play a significant role in improving awareness and understanding of digital dental technologies. This difference may be explained by the greater exposure of clinical students to teledentistry and AI-based digital health applications during clinical practice. In addition, it has been emphasised that knowledge levels in teledentistry should be supported through structured educational programmes, coursework, and professional seminars.
The literature indicates that knowledge levels regarding teledentistry are associated with educational exposure and access to digital information sources [20]. In particular, greater interaction with digital technologies among younger individuals may contribute to relatively positive awareness and attitudes toward teledentistry, even when knowledge levels are limited [22]. However, it has been reported that the limited availability of structured and systematic teledentistry education in dental training may hinder the achievement of adequate knowledge levels [23]. Consistent with the existing literature, the results of this study suggest that dental students’ knowledge of teledentistry could be improved, while their awareness and attitudes toward it are relatively positive. While 52.1% of students reported having no knowledge of teledentistry, those who indicated that they were knowledgeable most commonly reported social media as their primary source of information. The widespread use of social media may help explain why students access information about teledentistry through these channels. At the same time, students’ use of various digital platforms and educational resources highlights the role of multiple information sources in this field. Notably, among clinical students, social media was reported more frequently as a source of information than undergraduate seminars. This finding is consistent with previous studies reporting that dental students increasingly use social media platforms as supplementary educational resources and sources of professional information, often alongside or in place of traditional educational activities [24, 25]. This finding may reflect the insufficient integration of teledentistry-related content into formal dental education and may indicate that undergraduate dental curricula have not yet fully adapted to the rapidly evolving digital transformation in healthcare. The widespread reliance on social media and digital platforms for teledentistry-related information suggests that students may seek more accessible, up-to-date, and technology-oriented learning resources outside formal education. However, although digital platforms may facilitate rapid access to information, they may also increase the risk of inconsistent or non-evidence-based knowledge acquisition. This situation highlights the importance of integrating structured and evidence-based teledentistry and AI-related content into undergraduate dental education in order to support professional standards, ethical awareness, and safe clinical implementation.
The literature emphasises the need to integrate structured teledentistry modules into dental curricula, including simulation-based training and interdisciplinary approaches, to improve students’ preparedness and confidence in diagnosing and managing different oral health conditions [17, 26–28]. In the present study, students’ self-assessment of their teledentistry knowledge as insufficient may suggest a potential need for more structured educational opportunities in this area. In this context, the inclusion of systematic teledentistry education at the undergraduate level may contribute to improving students’ knowledge and awareness.
The literature indicates that teledentistry strengthens patient–dentist communication from the patient perspective [29]. Similarly, in the present study, the majority of dental students reported that teledentistry would positively influence the dentist–patient relationship and facilitate patients’ access to dental care. These findings are consistent with previous literature highlighting the potential of teledentistry to enhance communication and improve access to healthcare services [29]. However, a previous qualitative study has also reported that some patients may feel uncomfortable during remote consultations and may prefer face-to-face examinations, particularly because of concerns related to communication and diagnostic accuracy [30]. This difference may be explained by the fact that the present study evaluated students’ perceptions and expectations regarding teledentistry rather than direct patient experiences during remote clinical consultations. Özkan Ata et al. [21] also demonstrated positive attitudes toward teledentistry among dental professionals, including dental students. In a study conducted by Akçiçek et al. [13] among undergraduate students, a significant association was found between academic year and attitudes toward teledentistry, with senior students exhibiting more positive attitudes. Consistent with these findings, clinical-level students in the present study reported more positive views regarding the potential impact of teledentistry on the dentist–patient relationship compared with preclinical students (p < 0.05). This may be attributed to the greater experience of clinical students in patient management and communication, which may lead them to evaluate the potential contributions of teledentistry to the dentist–patient relationship more favourably. Nevertheless, the majority of participants believed that teledentistry cannot replace conventional dental practice (61.7%).
The literature reports that the applicability of teledentistry may be limited, particularly in developing countries and rural areas, due to technological challenges such as inadequate or unreliable internet infrastructure [31]. Various barriers affecting the implementation of teledentistry have been identified, including infrastructural limitations, as well as factors related to technical competence, ethical concerns, and cost. In the present survey study, participants also reported several challenges, with infrastructure-related issues being among the most frequently cited barriers, consistent with previous findings [32, 33]. The slow adoption of teledentistry has been attributed in the literature to multiple factors, including levels of professional preparedness, limited technical resources, financial and legal uncertainties, and shortcomings in educational training [34, 35]. Similarly, the findings of the present study indicate that students perceive the barriers to the widespread use of teledentistry within technical, educational, and legal frameworks. While participants primarily emphasised technological infrastructure issues, concerns related to insufficient knowledge, patient safety, and data privacy were also noteworthy. In a clinical study evaluating the implementation of video-based teledentistry approaches among patients requiring special care, participants were generally satisfied with remote care applications; however, concerns regarding the accuracy and effectiveness of remote examinations compared with face-to-face visits were also identified [36]. These findings may support the view that, although teledentistry is often perceived positively, concerns regarding technical reliability and clinical applicability may still limit its broader acceptance and implementation.
The literature indicates that dentists prefer synchronous communication methods, such as video conferencing and messaging, for managing digital appointments, with video conferencing in particular emerging as a prominent method for early diagnosis and the assessment of dental caries prevalence [37–41]. The findings of the present study are consistent with these reports, showing that participants most frequently preferred video conferencing and teleconsultation as the primary communication technologies in teledentistry, followed by AI and machine learning application. In addition, participants expressed the need for further development of teledentistry through AI-supported diagnostic systems, online appointment and patient follow-up applications, and more secure data-sharing infrastructures. However, despite the growing interest in AI-supported teledentistry, previous literature has also highlighted several unresolved challenges in this field, including the limited availability of large-scale clinical validation studies, concerns regarding data privacy and ethical issues, and the potential risk of bias in AI algorithms [42, 43]. These considerations may partially explain why, despite generally positive attitudes toward AI-supported technologies, some students in the present study remained hesitant regarding the broader implementation of teledentistry in routine clinical practice. In addition, qualitative research has highlighted practical and contextual barriers affecting teledentistry adoption, including inadequate infrastructure, limited formal training, low digital literacy, patient dissatisfaction with remote consultations, and diagnostic limitations [30]. Together, these findings suggest that technical, educational, and clinical challenges may continue to affect the wider integration of teledentistry into routine dental care.
It has been shown that dental students most frequently prefer video phone calls and computer-based video consultations, followed by email, messaging, and voice calls [13]. Similarly, in the present study, participants most frequently selected video conferencing and teleconsultation among the technologies used in teledentistry.
When asked about technologies expected to play a significant role in the future of teledentistry, the majority of participants highlighted AI. Survey results indicate that dental students believe AI will be an important component of teledentistry. This perception is consistent with the literature regarding the transformative potential of AI in teledentistry [44] and may be considered a positive indicator for future clinical adoption. The positive perception toward AI observed in the present study are consistent with previous literature reporting that dental students generally perceive AI as a useful tool in dental practice [45]. In a previous study conducted among dental students, participants reported favorable opinions regarding the potential contribution of AI, and the authors noted that students demonstrated more moderate and balanced perspectives compared with earlier literature [46]. These findings may suggest increasing familiarity and acceptance of AI-supported technologies among dental students.
A study examining dentists’ perceptions of teledentistry have reported that teledentistry is considered beneficial, particularly for patients and dental practices [20]. Similarly, in the present study, the majority of participants expressed positive views regarding the benefits of teledentistry. However, despite these positive perceptions, most students reported being undecided about using teledentistry in their future professional careers (68.8%). This finding suggests that although students recognise the potential benefits of teledentistry, they remain hesitant about its future use in clinical practice. Similar findings have been reported in previous studies, where dental students expressed positive attitudes toward teledentistry while simultaneously reporting limited knowledge, practical experience, and preparedness for its implementation in clinical settings [13, 47]. In contrast, students demonstrated considerable interest in the future role of AI in teledentistry. This may indicate that while students perceive AI use within teledentistry as promising and innovative, they still have concerns regarding the practical implementation, legal responsibilities, technical infrastructure, and reliability of teledentistry in routine clinical care. This contrast highlights the need for more comprehensive educational and practical training on teledentistry and AI use within teledentistry during undergraduate dental education.
At present, teledentistry functions primarily as a complementary approach rather than a replacement for face-to-face clinical examinations. However, with rapid advances in AI technologies, dental care is expected to shift from a reactive, treatment-oriented model toward a predictive, preventive, and patient-centred approach. This transformation is anticipated to have a profound impact not only on clinical practice but also on dental education and research [44]. In this context, the findings of the present study suggest that integrating digital literacy and AI-related content into dental curricula may help future dentists use these technologies more effectively and safely. Although the present study cannot directly demonstrate improvements in diagnostic reliability or clinical effectiveness, the findings indicate that students generally perceive AI-supported teledentistry as a promising component of future dental care. In the near future, AI-supported teledentistry may play an important role in expanding access to oral healthcare, particularly for disadvantaged and geographically isolated populations.
The findings of this study may contribute to understanding dental students’ perspectives on teledentistry and AI-supported systems. However, given the single-center and cross-sectional design of the study, broader conclusions regarding curriculum development should be interpreted cautiously. The findings should also be considered in light of several methodological limitations, including the use of self-reported questionnaire data, the absence of a formal validation process for the questionnaire, and the potential for selection and response bias. Further multicentre and longitudinal studies involving larger and more diverse samples are needed to better evaluate the educational and clinical implications of teledentistry and AI-supported systems in dental education.
Conclusions
This study demonstrates that although dental students’ knowledge of teledentistry and AI–based applications remains limited, their awareness and attitudes toward these technologies are generally positive. Higher knowledge levels observed among clinical students suggest that clinical exposure may enhance readiness for AI-supported teledentistry. The findings suggest a potential need for greater inclusion of structured teledentistry and AI-related content in undergraduate dental education to support future clinical adaptation and digital competency development.
Supplementary Information
Acknowledgements
The authors declare that no individuals other than the authors contributed to this study and that no financial or material support was received.
Abbreviations
- AI
Artificial Intelligence
Authors’ contributions
B.B.Ş. has participated in study design, methodology, investigation, writing, reviewing, and editing the manuscript. M.B. has participated in study design, methodology, investigation T.B. has been responsible for the statistical analyses.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability
Data available on request from the authors.
Declarations
Ethics approval and consent to participate
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008. Informed consent was obtained from all participants for being included in the study.’ The study was conducted at Sakarya University Faculty of Dentistry, was reviewed and approved by the Sakarya University Faculty of Medicine Scientific Research Ethics Committee (Approval date: November 18,2025, decision no: 605, Document No: E-43012747-050.04-533683).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Data Availability Statement
Data available on request from the authors.