ABSTRACT
Introduction
As artificial intelligence (AI) rapidly evolves in dentistry, understanding dentists' and dental students' perspectives is key. This survey evaluated Australian dentists' and students' attitudes and perceptions of AI in dentistry.
Methods
An online questionnaire developed on Qualtrics was distributed among registered Australian dentists and students enrolled in accredited Australian dental or oral health programmes. Descriptive and bivariate analyses were used to examine the demographic variables and participant attitudes.
Results
177 responses were received, and 155 complete responses were used in data analysis. 54.8% were aware of dental AI applications, but 70.3% could not name a specific AI software. A majority (91.6%) viewed AI as a supportive tool, with 69% believing that it would be beneficial in clinical tasks and 35.6% expecting it to perform similarly to an average specialist. 40% anticipated that dental AI would be routinely used in the next 5–10 years, with more dental students expecting this short‐term integration. Concerns included job displacement, inflexibility in patient care, and mistrust of AI's accuracy. Attitudes towards AI were influenced by age, gender, clinical experience and technological proficiency.
Conclusions
The survey underscores the potential of AI to revolutionise dental care, enhancing clinical workflows and decision‐making. However, challenges like trust in AI and ethical concerns remain. It is recommended that practising dentists receive hands‐on training with AI tools and continuing dental education programmes. Integrating AI into dental curricula and fostering interdisciplinary teaching and research collaborations between computer science and dentistry is necessary to prepare graduates to use AI effectively and responsibly.
Keywords: artificial intelligence, attitudes, dentistry, perception, survey
1. Introduction
Artificial intelligence (AI) refers to machines that imitate human knowledge and behaviour [1]. The term artificial intelligence was first described by John McCarthy in 1955 [2]. In the last two decades, there has been a significant increase in research on applications of AI technologies in healthcare [3]. In 2023, AI in healthcare was worth 14.6 billion US dollars, and the market share is expected to be worth almost 102.7 billion US dollars by 2028 (https://www.marketsandmarkets.com/Market‐Reports/artificial‐intelligence‐healthcare‐market‐54679303.html, https://www.statista.com/statistics/1334826/ai‐in‐healthcare‐market‐size‐worldwide/). The extensive use of electronic health records and digital imaging has facilitated the availability of huge data sets that has propelled the success of AI applications in healthcare [4, 5].
In clinical dentistry, AI technologies have been developed to aid in the radiologic diagnosis of different oral and maxillofacial pathologies and provide decision support and analysis of treatment outcomes in various dental disciplines [6, 7, 8, 9, 10, 11, 12, 13, 14, 15]. Several commercially available AI technologies are helpful in routine dental care and reliable tools in providing a second opinion for image and patient data, improving clinical efficiency and thus saving valuable clinical time [16, 17, 18, 19, 20, 21, 22] (e.g., PearlAI [16] in caries detection). AI can process various data types, such as patient history, demographic information and treatment records, to help identify patient preferences and specific needs and boost motivation and accountability towards their health. AI‐enabled smart assistants and chatbots can support dental consultations through teledentistry [23]. Despite these challenges, the future implications of AI are promising. AI has the potential to streamline clinical workflows, standardise diagnosis and treatment, increase the quality of clinical decisions, and improve patient safety by reducing errors [24]. AI can streamline administrative tasks in clinical practice, and this can free up time and resources for more patient‐centred initiatives, making healthcare more accessible and affordable [25]. AI‐enabled virtual and augmented reality (VR and AR) devices and gamification techniques can also assist with student training [26].
Although AI holds significant promise, its routine adoption in dental practice remains limited. Challenges that impede wider adoption include data accessibility, lack of replicability and robustness in dental AI research, and limited capabilities of current AI applications [5]. Additionally, there are concerns about data privacy and security, lack of generalisability of the algorithms, and inherent bias that may skew diagnostic or treatment recommendations [5, 27]. The opaque ‘black‐box’ nature of the algorithm design has raised concerns about transparency and explainability in the decisions made by AI algorithms [28]. In addition, overreliance on technology may diminish the human element in dental care, impacting the quality and comprehensiveness of care delivered [23].
New avenues in AI research have been explored to address the concerns and translate research into effective clinical tools. This latest research includes understanding the interactions between clinicians and AI technologies. It also involves assessing the impact of AI technologies on the quality, efficiency and productivity of clinical practice and understanding how users adopt new technologies such as AI [29, 30]. User acceptance and experience can significantly influence attitudes and perceptions towards adopting new technology [30, 31, 32]. Based on the perceptions of potential users, evaluations have been conducted to identify usability issues, gaps in understanding, and possible barriers and concerns associated with AI applications in medicine and dentistry [33, 34, 35, 36, 37, 38].
Evidence from recent literature suggests that the adoption of new technologies, particularly AI, can vary significantly among different healthcare settings, which are affected by the societal and cultural contexts and the clinical workflows [39, 40, 41]. Understanding the user experience within the Australian context enables the customisation of AI applications, promoting trust and acceptance among dentists and dental students. This understanding ensures that AI technologies align with local workflows, regulatory frameworks and patient expectations, maximising their effectiveness and utility [42].
Consequently, it is crucial to understand the perceptions and expectations of the Australian dental profession regarding AI technologies. This understanding will facilitate a better grasp of how and when these technologies will be adopted and implemented in routine dental care. This survey aimed to study the attitudes and perceptions of Australian dental practitioners and students about the applications of artificial intelligence in dental practice.
2. Methods
A cross‐sectional survey was designed and distributed through the online Qualtrics platform (www.qualtrics.com). The study received ethics approval (Human Research Ethics Committee approval number 2021/454) and was conducted in accordance with the Declaration of Helsinki [43]. The following inclusion and exclusion criteria were used to invite the participants:
Inclusion criteria:
Dentists, dental specialists and oral health therapists registered to practice in Australia
Dental and oral health students studying in any accredited Australian university
Exclusion criteria:
Dental practitioners other than dentists, dental specialists and oral health therapists
Dental practitioners not registered to practice in Australia
The dental students studying at Australian universities were contacted by an announcement in their University's online learning management systems. Dentists and dental specialists were contacted through the research team's known networks and passive snowball recruitment [44]. Social media platforms, including Facebook and LinkedIn, were also used to circulate the anonymised survey link. Participation was voluntary, and no incentives were offered. Informed consent was considered when participants completed and submitted the survey. The survey remained open from September 2021 until August 2022. Further data collection was stopped when regular monitoring and analysis indicated data saturation. This approach was adopted based on the grounded theory in qualitative research [45].
The survey consisted of an anonymised questionnaire containing multiple choice, Likert scale and open‐ended questions. The research team developed the questionnaire based on the literature review and similar previously published surveys [33, 37]. The questionnaire was designed to present appropriate questions to dental practitioners and students using the branching option within Qualtrics. The questions covered the participants' awareness and perceptions about the current applications of AI algorithms in dentistry, their impact on dental workflow and the dental curriculum, potential benefits of AI‐based solutions in dentistry, performance and accuracy of dental AI applications and concerns about adopting AI solutions in dentistry.
Demographic questions included participants' age, gender, professional qualifications (category), type of practice, years of clinical experience and location of practice (metropolitan/rural/private/public/academia). Student participants were asked to indicate the type (oral health or dental) and entry (undergraduate or postgraduate) of the programme they were enrolled in. The questionnaire was pilot tested to ensure that the questions were relevant, appropriate and accessible. The survey questionnaire is included in Data S1.
Data were transferred from the survey platform to IBM's SPSS statistics software (version 26, IBM, SPSS Inc., Chicago, IL) and analysed. Descriptive statistics summarised the demographic characteristics of the survey respondents. The association between the categorical variables was assessed using the chi‐squared test of independence. Fisher's exact test was used when more than 20% of the cells had an expected frequency of less than 5. The Spearman rank correlation test was used to assess the correlation between variables. The level of significance for all tests was set at p < 0.05.
3. Results
A total of 177 responses were received, and after removing incomplete records, 155 responses (87.6%) were used in the data analysis. Table 1 summarises the demographic characteristics of the participants. Of the 155 participants, 64 were dental students (43.2%), seven were oral health students (4.7%) and 77 were registered dentists (52%). The participants' ages ranged from 22 to 85 years (average of dental students 25 ± 3.7 years; dentists 37.8 ± 14.8 years) (Figure 1), with equal gender distribution. On average, the dentists practised 4.4 days per week, and most of the dentists practised in the private sector (n = 42, 27.1%) and in metropolitan areas (n = 52, 33.5%). The clinical experience of the dentists ranged from 1 to 43 years, with an average of 12.6 (± 11.8) years.
TABLE 1.
Demographic data.
| Participant's qualifications, n (%) | |
| Dental students | 55 (35.5) |
| Oral health students | 18 (11.6) |
| General dentists | 68 (43.9) |
| Specialist dentists | 11 (7.1) |
| Data missing | 03 (0.9) |
| Participant's gender, n (%) | |
| Males | 73 (47.1) |
| Females | 73 (47.1) |
| Data missing | 9 (5.8) |
| Participant's age (years) | |
| Mean (±SD) | 32.3 ± 12.5 |
| Minimum | 18 |
| Maximum | 85 |
| Years in clinical practice (years) | |
| Mean (±SD) | 12.6 ± 11.8 |
| Minimum | 1 |
| Maximum | 43 |
| Working days per week (mean ± SD) | 4.4 ± 1.3 |
| Type of practice (choice count) a | |
| Metropolitan | 52 (33.5) |
| Rural | 13 (8.4) |
| Public | 15 (9.7) |
| Private | 42 (27.1) |
| Academia | 12 (7.7) |
The total does not add up to 100% as the participants chose more than one option.
FIGURE 1.
Dental student and practitioner age range.
Among 146 valid responses, participants' self‐assessment of their technological proficiency was 4.02 on a scale of 0–5 (Figure 2), with similar average scores for dental students and practitioners. Dentists used several digital technologies in their clinic, such as digital X‐ray imaging (45.2%), digital patient records (43.9%) and OPG machines (38.7%).
FIGURE 2.
Participant's self‐assessment of technological proficiency.
3.1. User Experience and Acceptance of Dental AI Applications in Clinical Practice
Among the survey participants, 54.8% (n = 85) were aware of AI applications in the field, and they mainly learned about dental AI applications through lectures, conferences, journals and social media (Table 2). Interestingly, a majority (n = 109, 70.3%) were unable to name a specific dental AI software, with a slightly higher proportion of student participants (n = 59, 54%) compared to dentists (n = 47, 43%) in this category. Among the 29.7% (n = 46) who could name a specific dental AI software, their knowledge primarily came from scholarly and clinical sources. The majority of participants identified dentomaxillofacial radiology (64.5%) and implantology (64.5%) as the most amenable disciplines for AI applications (Figure 3). This finding was augmented by the participant's preferences for AI use across various other areas, including diagnostics, general dentistry and emergency triage, suggesting a broad scope for practical integration of AI. AI was found to be most beneficial for tasks such as image processing and diagnosis and less beneficial for administrative tasks and treatment planning.
TABLE 2.
Differences in the responses among dentists and dental students.
| Have you heard of AI in dentistry? | ||||
|---|---|---|---|---|
| Student or dental practitioner | Yes, n (%) | No, n (%) | Total, n (%) | p |
| Dental student | 32 (45.1) | 39 (54.9) | 71 (100) | 0.023 |
| Dental practitioner | 49 (63.6) | 28 (36.4) | 77 (100) | |
| Total | 81 (54.7) | 67 (45.3) | 148 (100) | |
Note: Bold value denote statistical significance at the p < 0.05.
FIGURE 3.
Dental disciplines most likely to benefit from AI.
AI was recognised as a supportive tool for clinicians by 91.6% (n = 142) of the participants, and only 2% (n = 3) had concerns about potential negative effects. A majority of the participants (n = 107, 69%) indicated that AI would be beneficial to clinical tasks in dentistry. However, 6% (n = 9) indicated that either AI would make no difference or have a negative impact. The participants revealed diverse opinions on the expected performance of AI compared to specialists: 35.6% (n = 52) expected AI performance to equal an average specialist, while 19.9% (n = 29) thought AI could outperform the best specialist. Additionally, 23.3% (n = 34) saw AI on par with the least effective specialist, 13% (n = 19) with the best and 8.2% (n = 12) predicted that AI would surpass the top specialists. There was no significant difference in expectations between dentist and student participants (p > 0.05).
Regarding the timeline for AI integration, 40% (n = 60) of all participants indicated that AI applications would be routinely used in dentistry within the next 5–10 years (Figure 4), with similar opinions among dentists (n = 29, 48.3%) and dental students (n = 31, 51.6%). Interestingly, 23.4% (n = 18) of dentist participants, compared to only 11.3% (n = 8) of student participants, considered AI to be already integrated into dental practice. In comparison with dentists, dental students anticipated AI integration in clinical practice in the short term (p = 0.022). Over a third of dentist participants (n = 53, 34.2%) expressed excitement about integrating AI into their practice, whereas 17.4% (n = 27) believed AI would make little difference or had reservations. When faced with a discrepancy between AI's judgement and their own, a majority of participants, 59.6% (n = 87), indicated they would consult a colleague or an experienced clinician, whereas 8.9% (n = 13) indicated they would resort to other measures, such as searching scientific literature to resolve the differences (Figure 5). More students (n = 46, 68.7%) than dentists (n = 35, 48.6%) indicated they would refer to a colleague or a senior clinician. In contrast, more dentists (n = 27, 37.5%) indicated they would trust their own judgement compared to dental students (n = 15, 22.4%).
FIGURE 4.
Timeline for integration of AI in dentistry. (A) All participants. (B) Distribution of responses among dentists and dental students.
FIGURE 5.
Participant's preferences when their clinical decision differs from the AI decision. (A) All participants. (B) Participants' responses as a dental student or practitioner.
The survey also investigated the concerns about AI applications. The primary concerns identified were job losses to more efficient technology, lack of flexibility in patient care and insurance liability. Additionally, mistrust in the technology and concerns about its accuracy were highlighted. The survey found no statistically significant differences in the responses to concerns about AI applications among dentists and dental students.
3.2. Factors Affecting Attitudes Towards Dental AI Applications
The survey suggested that attitudes towards AI in dentistry were shaped by factors such as age, gender, clinical experience, professional qualifications and technological proficiency. Participants who were aware of dental AI applications were significantly older (mean age 35.4 ± 14.81 years) compared to the participants who were not aware of these applications (mean age 28.61 ± 7.35 years) (p < 0.05). Participants who believed AI's routine use was imminent within the next 5–10 years were older than participants who saw AI as already integrated or expected it to happen within 5 years. Gender differences were observed in the expectations for AI performance and its role in clinical support. More females viewed AI as a clinical tool (n = 48, 65.8%) and relied on their judgement over AI in discrepancies (n = 24, 32.9%) compared to males. Male participants were more inclined to use AI applications if available within the year (M = 19, 24%; F = 11, 14%).
Participants familiar with AI applications reported more clinical experience than those unfamiliar (p = 0.02) (Figure 6). Awareness of specific software was higher among specialist dentists compared to GPs (p < 0.01). In addition, specialists (n = 6, 54.5%) had a more positive attitude towards AI compared to general practitioners (n = 11, 16.2%), likely due to higher exposure to AI and its applications within their field (p < 0.01). Additionally, those who perceived themselves as technologically proficient reported higher awareness of AI's dental applications (p < 0.01). However, this self‐assessment about technological proficiency did not significantly influence opinions on the timeline for AI integration in dentistry.
FIGURE 6.
Association between dentist's years in clinical practice and awareness of dental AI applications.
3.3. Correlations Among Survey Variables
Significant correlations were observed between the responses received from the participants (Table S1, Data S1). A statistically significant moderate correlation (r = 0.42) was observed between the participant's awareness of a specific AI software and their likelihood of using AI if it became available in the next year. Participants' perceptions of the impact of AI on dental practice and workflows also correlated with the likelihood of using AI technologies if they became available within the year (r = 0.53, p < 0.05). A weak correlation between participants' perceptions of the timeline of AI integration and their technological proficiency and clinical experience was observed (p < 0.05). The negative correlation between the participant's age and their opinion about the timeline for AI integration into dental practice suggested that younger participants anticipated an earlier adoption of AI applications in dentistry.
4. Discussion
This survey investigated the user experience and acceptance to understand the knowledge and perceptions of Australian dentists and dental and oral health students towards applications of AI technologies in dentistry. Although several studies [25, 29, 34, 35, 36, 37, 38] have reported the perceptions regarding AI applications, understanding user experience and acceptance is limited in medicine and dentistry, particularly in the Australian context. With an increase in the applications of AI technologies in our day‐to‐day lives, such as chatbots, AI smart assistants and autonomous vehicles, these technologies are becoming increasingly familiar. So, it is no surprise that a majority of our participants were familiar with AI applications. However, there was a notable gap in knowledge about specific dental AI software, likely due to the limited adoption of AI technologies in clinical practice. Regarding the reliance and trust in dental AI applications, the results indicated a cautious optimism among participants about integrating AI into their practices. While there is a general willingness to adopt AI technologies, hesitance persists, reflecting the need for more tangible demonstrations of the effectiveness of AI in clinical settings. Concerns about data protection and confidentiality [47] have already been identified, and governments worldwide have recognised this concern and created policies such as the EU's AI Act and Australia's Privacy Principles [48, 49]. Our findings are similar to those of other studies conducted in dental communities in other parts of the globe [33, 35, 46, 50, 51]. Across these studies, there is a generally positive attitude towards the potential benefits of AI in dentistry, such as improved diagnostic accuracy, efficiency and treatment planning. The knowledge regarding dental AI applications varied among dental students and practitioners in these studies. Lack of technical resources and training, concerns about data privacy, and algorithmic bias were identified as barriers to adopting AI technologies in these studies. The findings indicated a need for more focused educational efforts to enhance the awareness and understanding of dental AI applications as well as their capabilities and limitations.
The survey revealed that AI applications were perceived to have a broad scope for integration, particularly in dentomaxillofacial radiology and implantology. The specialisation of current AI algorithms in specific tasks such as caries detection limits its extensive use and integration in clinical practice [29]. However, with the fast‐paced research and development in this area, we can expect the availability of more comprehensive AI applications that encompass all aspects of dentistry.
Our survey found that while there was considerable openness to adopting AI technologies in dental practice, there was some scepticism about AI decisions, where the participants preferred to consult a colleague or a senior clinician rather than rely on AI. It emphasises the need for AI technologies to be designed to augment rather than replace dentist's expertise. The participants' concerns about the impact of AI technologies on job security are valid and may stem from the potential for AI to automate certain aspects of dentistry [52]. However, it is essential to note that dentistry is a highly skilled profession and relies heavily on human expertise, empathy and nuanced judgement [53, 54]. There is a possibility that AI technologies will redefine job roles in dentistry, allowing dentists to focus on more complex cases or aspects of patient care. This misapprehension by dentists can be overcome by acquiring new skills related to AI and understanding how AI tools can enhance dental practice. The survey participants identified a lack of flexibility in the AI's decisions as a concern. The ability to make nuanced treatment decisions considering a patient's overall context, including quality of life, socioeconomic background, emotional well‐being and personal preferences, is a uniquely human trait. These human experiences are complex, subtle and difficult to quantify and integrate into AI algorithms, making them incapable of incorporating the broader context of a patient's life into treatment decisions. For AI to be more widely adopted, robust regulatory and ethical guidelines are essential for safe clinical practice [55].
The differences in the perceptions between dentists and students underscore the importance of considering the role of professional experience in shaping perceptions of technological advancements. These findings reflect the findings in a recent systematic review conducted on the perceptions of dental students and practitioners regarding dental AI applications [56]. Dentists will likely view new technologies like AI through the lens of how these innovations fit into their practices. In contrast, students still in the formative stages of their careers may not have ingrained views or opinions. Understanding these differences is crucial for effectively communicating about and implementing AI technologies in dental practice and tailoring AI‐related education and training for current and future dental professionals. It would be advantageous to conduct longitudinal studies to investigate the integration of AI into dental curricula and assess the effectiveness of different training methods. In addition, research into the impact of demographic factors (ethnic and cultural background of participants, clinician experience) and the long‐term effects of AI integration in dentistry on diagnostic accuracy, treatment efficacy, patient outcomes and perceptions will provide deeper insight into the impact of AI technologies on clinical practice.
This study was limited by the sample size and the lack of representation of various demographic categories, including the cultural and ethnic backgrounds of participants. The voluntary nature of the survey may have resulted in selection bias, as only those participants with an interest in this topic may have responded to the survey. In addition, the self‐reported data obtained from surveys are subject to response bias, the influence of the environment and context in which the survey was undertaken, leading to biased responses.
5. Conclusion
The survey explored the user experience and acceptance to understand the current landscape of dental AI applications in Australia. The awareness of dental AI among dental students and practitioners is high. Our study suggests that the best use of dental AI applications would be as a support system that can provide data‐driven insights, allowing dentists to focus on more patient‐centred aspects of dental practice. Despite the concerns about the impact of AI on jobs and patient care, the participants foresee the integration of AI in dental care and professional practice in the near future. The study also recognised the various factors that affected the participant's attitudes towards AI. The opinion of the main stakeholders, including dental practitioners, students, education providers, policymakers and patients, is essential as it will significantly affect the integration of AI technologies in dentistry. Future strategies for AI implementation should consider ethical and regulatory challenges. Consequently, dental education and training programmes must adapt to include AI literacy, preparing dental practitioners to confidently and efficiently utilise AI technologies.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Data S1.
Table S1.
Acknowledgements
Open access publishing facilitated by The University of Sydney, as part of the Wiley ‐ The University of Sydney agreement via the Council of Australian University Librarians.
Funding: The authors received no specific funding for this work.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
References
- 1. Shan T., Tay F. R., and Gu L., “Application of Artificial Intelligence in Dentistry,” Journal of Dental Research 100, no. 3 (2020): 232–244, 10.1177/0022034520969115. [DOI] [PubMed] [Google Scholar]
- 2. Rajaraman V., “JohnMcCarthy—Father of Artificial Intelligence,” Resonance 19, no. 3 (2014): 198–207, 10.1007/s12045-014-0027-9. [DOI] [Google Scholar]
- 3. Bohr A. and Memarzadeh K., “The Rise of Artificial Intelligence in Healthcare Applications,” in Artificial Intelligence in Healthcare (Cambridge, MA: Academic Press, 2020), 25–60. [Google Scholar]
- 4. Sarker I. H., “Machine Learning: Algorithms, Real‐World Applications and Research Directions,” SN Computer Science 2, no. 3 (2021): 160, 10.1007/s42979-021-00592-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Schwendicke F., Samek W., and Krois J., “Artificial Intelligence in Dentistry: Chances and Challenges,” Journal of Dental Research 99, no. 7 (2020): 769–774, 10.1177/0022034520915714. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Lee J. H., Kim D. H., Jeong S. N., and Choi S. H., “Diagnosis and Prediction of Periodontally Compromised Teeth Using a Deep Learning‐Based Convolutional Neural Network Algorithm,” Journal of Periodontal & Implant Science 48 (2018): 114–123. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Kavitha M. S., Asano A., Taguchi A., Kurita T., and Sanada M., “Diagnosis of Osteoporosis From Dental Panoramic Radiographs Using the Support Vector Machine Method in a Computer‐Aided System,” BMC Medical Imaging 12, no. 1 (2012): 1–11, 10.1186/1471-2342-12-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Frydenlund A., Eramian M., and Daley T., “Automated Classification of Four Types of Developmental Odontogenic Cysts,” Computerized Medical Imaging and Graphics 38, no. 3 (2014): 151–162, 10.1016/j.compmedimag.2013.12.002. [DOI] [PubMed] [Google Scholar]
- 9. Okada K., Rysavy S., Flores A., and Linguraru M. G., “Noninvasive Differential Diagnosis of Dental Periapical Lesions in Cone‐Beam CT Scans,” Medical Physics 42, no. 4 (2015): 1653–1665, 10.1118/1.4914418. [DOI] [PubMed] [Google Scholar]
- 10. Chang S. w., Abdul‐Kareem S., Merican A. F., and Zain R. B., “Oral Cancer Prognosis Based on Clinicopathologic and Genomic Markers Using a Hybrid of Feature Selection and Machine Learning Methods,” BMC Bioinformatics 14, no. 1 (2013): 170, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3673908&tool=pmcentrez&rendertype=abstract. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Khanagar S. B., Al‐Ehaideb A., Maganur P. C., et al., “Developments, Application, and Performance of Artificial Intelligence in Dentistry—A Systematic Review,” Journal of Dental Sciences 16, no. 1 (2021): 508–522, https://www.sciencedirect.com/science/article/pii/S1991790220301434. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Johari M., Esmaeili F., Andalib A., Garjani S., and Saberkari H., “Detection of Vertical Root Fractures in Intact and Endodontically Treated Premolar Teeth by Designing a Probabilistic Neural Network: An Ex Vivo Study,” Dento Maxillo Facial Radiology 46, no. 2 (2017): 20160107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Fukuda M., Inamoto K., Shibata N., et al., “Evaluation of an Artificial Intelligence System for Detecting Vertical Root Fracture on Panoramic Radiography,” Oral Radiology 36, no. 4 (2020): 337–343. [DOI] [PubMed] [Google Scholar]
- 14. Saghiri M. A., Asgar K., Boukani K. K., et al., “A New Approach for Locating the Minor Apical Foramen Using an Artificial Neural Network,” International Endodontic Journal 45, no. 3 (2012): 257–265. [DOI] [PubMed] [Google Scholar]
- 15. Patil V., Vineetha R., Vatsa S., et al., “Artificial Neural Network for Gender Determination Using Mandibular Morphometric Parameters: A Comparative Retrospective Study,” Cogent Engineering 7, no. 1 (2020): 1723783. [Google Scholar]
- 16. Pearl AI , “Pearl—The Future of Dentistry, Powered by Dental AI,” (2022), accessed November 21, 2022, www.hellopearl.com.
- 17. Videa Health , “VideaHealth: The Dental AI Trusted by Dentists and DSOs,” (2022), accessed November 21, 2022, https://www.videa.ai/.
- 18. Denti AI , “Denti AI,” (2022), accessed November 21, 2022, https://www.denti.ai/.
- 19. ORCA Dental AI , “ORCA Dental AI: Quality of Dental Care With the Power of AI,” (2022), accessed November 21, 2022, https://www.orca‐ai.com/.
- 20. Glidewell.Io In Office Solutions , “Glidewell.Io in Office Solutions,” (2022), accessed November 21, 2022, https://glidewell.io/.
- 21. Smile Cloud Biometrics , “Smile Cloud Biometrics,” (2022), accessed November 21, 2022, https://www.smilecloud.com/#intuitive.
- 22. Dental Monitoring , “Dental Monitoring,” (2022), accessed November 21, 2022, https://dental‐monitoring.com/home‐3/.
- 23. Thorat V., Rao P., Joshi N., Talreja P., and Shetty A. R., “Role of Artificial Intelligence (AI) in Patient Education and Communication in Dentistry,” Cureus 16, no. 5 (2024): e59799. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Davenport T. and Kalakota R., “The Potential for Artificial Intelligence in Healthcare,” Future Healthcare Journal 6 (2019): 94–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Khalifa M., Albadawy M., and Iqbal U., “Advancing Clinical Decision Support: The Role of Artificial Intelligence Across Six Domains,” Computer Methods and Programs in Biomedicine Update 5 (2024): 100142, https://www.sciencedirect.com/science/article/pii/S2666990024000090. [Google Scholar]
- 26. Mahesh Batra A. and Reche A., “A New Era of Dental Care: Harnessing Artificial Intelligence for Better Diagnosis and Treatment,” Cureus 15, no. 11 (2023): e49319. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Pethani F., “Promises and Perils of Artificial Intelligence in Dentistry,” Australian Dental Journal 66, no. 2 (2021): 124–135, 10.1111/adj.12812. [DOI] [PubMed] [Google Scholar]
- 28. Ding H., Wu J., Zhao W., Matinlinna J. P., Burrow M. F., and Tsoi J. K. H., “Artificial Intelligence in Dentistry—A Review,” Frontiers in Dental Medicine 4 (2023): 1–13, https://www.frontiersin.org/articles/10.3389/fdmed.2023.1085251. [Google Scholar]
- 29. Kelly C. J., Karthikesalingam A., Suleyman M., Corrado G., and King D., “Key Challenges for Delivering Clinical Impact With Artificial Intelligence,” BMC Medicine 17, no. 1 (2019): 195, 10.1186/s12916-019-1426-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Taherdoost H., “A Review of Technology Acceptance and Adoption Models and Theories,” Procedia Manufacturing 22 (2018): 960–967, https://www.sciencedirect.com/science/article/pii/S2351978918304335. [Google Scholar]
- 31. Rahimi B., Nadri H., Lotfnezhad Afshar H., and Timpka T., “A Systematic Review of the Technology Acceptance Model in Health Informatics,” Applied Clinical Informatics 9, no. 3 (2018): 604–634. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Distler V., Lallemand C., and Koenig V., “How Acceptable is This? How User Experience Factors can Broaden our Understanding of the Acceptance of Privacy Trade‐Offs,” Computers in Human Behavior 106 (2020): 106227, https://www.sciencedirect.com/science/article/pii/S0747563219304467. [Google Scholar]
- 33. Pauwels R. and Del Rey Y. C., “Attitude of Brazilian Dentists and Dental Students Regarding the Future Role of Artificial Intelligence in Oral Radiology: A Multicenter Survey,” Dento Maxillo Facial Radiology 50, no. 5 (2021): 20200461, 10.1259/dmfr.20200461. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Karan‐Romero M., Salazar‐Gamarra R. E., and Leon‐Rios X. A., “Evaluation of Attitudes and Perceptions in Students About the Use of Artificial Intelligence in Dentistry,” Dentistry Journal 11, no. 5 (2023): 125, https://www.mdpi.com/2304‐6767/11/5/125. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Abouzeid H. L., Chaturvedi S., Abdelaziz K. M., Alzahrani F. A., AlQarni A. A. S., and Alqahtani N. M., “Role of Robotics and Artificial Intelligence in Oral Health and Preventive Dentistry—Knowledge, Perception and Attitude of Dentists,” Oral Health & Preventive Dentistry 19, no. 1 (2021): 353–363, http://www.ncbi.nlm.nih.gov/pubmed/34259428. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Aboalshamat K. T., “Perception and Utilization of Artificial Intelligence (AI) Among Dental Professionals in Saudi Arabia,” Open Dentistry Journal 16, no. 1 (2022): 1–7. [Google Scholar]
- 37. Sit C., Srinivasan R., Amlani A., et al., “Attitudes and Perceptions of UK Medical Students Towards Artificial Intelligence and Radiology: A Multicentre Survey,” Insights Into Imaging 11, no. 1 (2020): 14, 10.1186/s13244-019-0830-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Roganović J., Radenković M., and Miličić B., “Responsible Use of Artificial Intelligence in Dentistry: Survey on Dentists' and Final‐Year Undergraduates' Perspectives,” Health 11, no. 10 (2023): 1480. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Cresswell K., Sullivan C., Theal J., Mozaffar H., and Williams R., “Concerted Adoption as an Emerging Strategy for Digital Transformation of Healthcare—Lessons From Australia, Canada, and England,” Journal of the American Medical Informatics Association 31 (2024): 1211–1215, 10.1093/jamia/ocae034. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Chen Y., Khalid Khan S., Shiwakoti N., Stasinopoulos P., and Aghabayk K., “Analysis of Australian Public Acceptance of Fully Automated Vehicles by Extending Technology Acceptance Model,” Case Studies on Transport Policy 14 (2023): 101072, https://www.sciencedirect.com/science/article/pii/S2213624X23001268. [Google Scholar]
- 41. Ullah F., Sepasgozar S. M. E., Thaheem M. J., and Al‐Turjman F., “Barriers to the Digitalisation and Innovation of Australian Smart Real Estate: A Managerial Perspective on the Technology Non‐adoption,” Environmental Technology & Innovation 22 (2021): 101527, https://www.sciencedirect.com/science/article/pii/S2352186421001759. [Google Scholar]
- 42. Shinners L., Aggar C., Grace S., and Smith S., “Exploring Healthcare professionals' Understanding and Experiences of Artificial Intelligence Technology Use in the Delivery of Healthcare: An Integrative Review,” Health Informatics Journal 26, no. 2 (2020): 1225–1236. [DOI] [PubMed] [Google Scholar]
- 43. World Medical Association , “Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects,” Journal of the American Medical Association 310, no. 20 (2013): 2191–2194. [DOI] [PubMed] [Google Scholar]
- 44. Goodman L. A., “Snowball Sampling,” Annals of Mathematical Statistics 32, no. 1 (1961): 148–170, http://www.jstor.org/stable/2237615. [Google Scholar]
- 45. Glaser B. G. and Strauss A. L., Discovery of Grounded Theory: Strategies for Qualitative Research (New York, NY: Transaction Publishers, 2017), https://www.frontiersin.org/articles/10.3389/fdmed.2023.1085251. [Google Scholar]
- 46. Yüzbaşıoğlu E., “Attitudes and Perceptions of Dental Students Towards Artificial Intelligence,” Journal of Dental Education 85, no. 1 (2021): 60–68, 10.1002/jdd.12385. [DOI] [PubMed] [Google Scholar]
- 47. Gianfrancesco M. A., Tamang S., Yazdany J., and Schmajuk G., “Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data,” JAMA Internal Medicine 178, no. 11 (2018): 1544–1547. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. European Commission , “AI Act,” 2024. Policies, https://digital‐strategy.ec.europa.eu/en/policies/regulatory‐framework‐ai.
- 49. Australian Government , “The Australian Privacy Principles,” 2020. Office of the Australian Information Commissioner, https://www.oaic.gov.au/assets/privacy/australian‐privacy‐principles/the‐australian‐privacy‐principles.pdf.
- 50. Eschert T., Schwendicke F., Krois J., Bohner L., Vinayahalingam S., and Hanisch M., “A Survey on the Use of Artificial Intelligence by Clinicians in Dentistry and Oral and Maxillofacial Surgery,” Medicina 58, no. 8 (2022): 1059. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51. Singh N., Pandey A., Tikku A. P., Verma P., and Singh B. P., “Attitude, Perception and Barriers of Dental Professionals Towards Artificial Intelligence,” Journal of Oral Biology and Craniofacial Research 13, no. 5 (2023): 584–588, https://www.sciencedirect.com/science/article/pii/S2212426823000854. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52. Vodanović M., Subašić M., Milošević D., and Savić P. I., “Artificial Intelligence in Medicine and Dentistry,” Acta Stomatologica Croatica 57, no. 1 (2023): 70–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53. Caplin R. L., “Dentistry—Art or Science? Has the Clinical Freedom of the Dental Professional Been Undermined by Guidelines, Authoritative Guidance and Expert Opinion?,” British Dental Journal 230, no. 6 (2021): 337–343. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54. Jeffrey D., “Empathy, Sympathy and Compassion in Healthcare: Is There a Problem? Is There a Difference? Does It Matter?,” Journal of the Royal Society of Medicine 109, no. 12 (2016): 446–452, 10.1177/0141076816680120. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55. Mennella C., Maniscalco U., De Pietro G., and Esposito M., “Ethical and Regulatory Challenges of AI Technologies in Healthcare: A Narrative Review,” Heliyon 10, no. 4 (2024): e26297. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56. Dashti M., Londono J., Ghasemi S., et al., “Attitudes, Knowledge, and Perceptions of Dentists and Dental Students Toward Artificial Intelligence: A Systematic Review,” Journal of Taibah University Medical Sciences 19, no. 2 (2024): 327–337, https://www.sciencedirect.com/science/article/pii/S1658361223002755. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data S1.
Table S1.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.