Exploring the impact of artificial intelligence on dental education: Benefits, challenges, and future directions – A systematic review of the literature

Abstract

The advent of artificial intelligence (AI) in dental education is a profound opportunity, spurred on by unprecedented technological change. It should be revamped in a way that supports immense capabilities, improves efficiency, and provides learning opportunities, but also has constraints that need to be understood. Thus, in this article, we discuss the benefits and challenges involved in adapting AI into dental education, as well as methods to improve the application of AI to enhance learning for students and instructors. We reviewed a total of 73 articles from multiple databases, including PubMed/Medline, Cochrane, Scopus, and Google Scholar. Through our review of articles from the past five years (2018–2024), the selected studies were evaluated based on relevance, recency, and credibility, which were sometimes difficult to distinguish between. We ultimately found 56 articles that met the above criteria for relevance. Using several combinations, we selected the relevant articles based on the keyword descriptions: AI in dentistry, AI in health care, deep learning in dentistry, machine learning in dentistry, AI in medical education, AI in dental education, AI constraints, and AI with dental students. Anything that was outdated, lacked credibility, or seemed to have redundant information was removed. Taken together, we reflect on the fact that AI has very exciting implications to innovate dental education, and the obstacles to broad-scale adoption can be overcome with responsible use and oversight. In conclusion, AI is a vital part of the contemporary dental curriculum and provides the potential for positive educational and clinical outcomes. AI provides an essential educational tool in the dental education curriculum for better teaching, learning, and patient care outcomes. If utilized effectively, then by identifying the limitations and guiding students on its ethical use, AI can narrow educational gaps and help students and mentors achieve better outcomes clinically and academically.

Introduction

Artificial intelligence (AI) was first coined by John McCarthy in 1956. His vision of AI was to develop machines that could act with the capacity to think, such as problem-solving, learning, and making decisions. In the context of AI, significant branches have emerged, including machine learning (ML), neural networks (NNs), deep learning (DL), and convolutional neural networks. Each of these branches is useful in multiple disciplines and has set the trend for solving complex problems and improving data analysis capabilities.[1,2] The rapid advancement of technology and AI has forever altered dental education and practice. Dentists and dental students utilizing AI innovations that speed up research and clinical decision-making are able to make better-informed decisions or create educational experiences more efficiently, and at the same time, more affordable for organizations to utilize. The delivery of better and quicker options is apparent utilizing AI innovations, and as the field progresses, it is important to recognize how far AI can go and the limitations and unintended consequences we may witness, as we envision autonomous, invisible, or unexplainable AI for the field of dentistry. Artificial neural networks (ANNs) attempt to replicate the structure and function of the human brain. Input signals are processed using weighted connections, where important information is given priority and fed into the ANN. Weighted inputs are summed together and served to an activation function, which determines an output, most commonly, ANN architectures.[3]

The use of UV light and spectroscopy in caries detection has been attracting considerable attention in dentistry. Many researchers have trained ANN models by using X-ray images to detect caries, with efficacy rates exceeding 99%. Most recently, researchers have successfully used feedforward backpropagation networks to detect Streptococcus mutans and, unsurprisingly, have combined adaptive algorithms (e.g., dragonfly and ANN classifiers), and similar models have promising results in caries detection.[4] Convolutional neural networks (CNNs) are also ideally suited to working with visual data. CNNs are advanced models in computer vision applications, including object recognition, image classification, and image segmentation, utilizing layers of convolutional operations to help extract a spatial hierarchy of features from images with little effort. CNNs automatically learn to extract features from images in an efficient manner. However, this does not imply that the technology is flawless; while AI models can yield excellent results, the outcome of AI models is typically modulated by data quality, the clinical environment, and the specific AI tool utilized.[5,6] Based on advancements made in AI for caries detection and monitoring, we understand that there are limitations and variability in diagnostic accuracies at this stage in the development of AI in dentistry.[7]

CNNs take grid-based data, e.g., images, and learn spatial features in a hierarchical or layered manner. Neural networks (NNs) process grid-based data, such as images, and learn spatial features in a hierarchical, layered manner. Most architectures consist of three key types of layers: convolutional layers and pooling layers for feature extraction, followed by fully connected layers that map the extracted features to the output. The design of CNNs gives them an advantage for processing images and data, and CNNs are becoming increasingly common in medical research.[8] The competencies of CNNs have achieved expert-level performance in important diagnostic procedures, including diabetic retinopathy screening, skin lesion classification, and cancer metastasis detection. Experiencing this transformation in treatment processes such as radiology and medical imaging emphasizes the role of CNNs in improving diagnostic capability and automating diagnostic procedures. In diagnostic healthcare, CNNs are essential digital technologies.[9,10,11]

As we look toward the future of dental education, it is essential to understand how AI can enhance our learning experiences while also recognizing the challenges it presents. This literature review aims to address two key research questions: first, what are the primary advantages of integrating AI into dental education, and second, what challenges and limitations does AI present within this context? This article seeks to explore the various ways AI can benefit students and educators, making teaching and learning more effective and engaging. This article is the first to add to the literature in exploring the literature concerning the impact of AI in dental education, an area that remains under-researched even as the technology gains prominence. Not only do we explain the benefits of AI, such as enhancing learning experiences and improving clinical results, but also the challenges and restrictions that accompany its deployment. By our focus on these domains, we aim to fill a glaring gap in existing scholarship, offering a comprehensive review that will inform educators, practitioners, and policymakers.

Materials and Methods

Search strategy and inclusion criteria

To conduct a comprehensive review of the literature on the impact of AI in dental education, we systematically searched several indexed databases, including PubMed/Medline, Cochrane, Scopus, and Google Scholar. The search focused on articles published between 2018 and 2024, and only English-language publications were considered. The primary keywords guiding our search included “dental education,” “artificial intelligence,” “dental technology,” “deep learning,” “health care,” and “patient care.”

Study participants and sampling

Studies with instructors and dental school students as the main participants were the focus of the review. A thorough search strategy was used, and 73 articles were found at first. Following a thorough screening procedure, 56 articles were chosen based on predetermined inclusion criteria, with a focus on current, pertinent, and methodologically sound research.

The PICO framework

We used the PICO framework to organize our review [Table 1].

Table 1.

PICO framework

PICO		Description
Population		Dental school students and instructors.
Intervention		Artificial intelligence technology integration and application in dental education
Comparison		Conventional teaching strategies devoid of AI incorporation
Outcome		Evaluation of the advantages, difficulties, and general influence on clinical results and learning

Search protocols

Our review involved a meticulous exploration of the various applications of AI in dental education. We employed multiple combinations of specific keywords, such as “AI in dentistry,” “AI in health care,” “DL in dentistry,” “ML” in dentistry,” “AI in medical education,” “AI in dental education,” “limitations of AI,” and “AI among dental students.” This strategic approach ensured a thorough collection of relevant literature, enabling us to capture a diverse range of perspectives and findings related to AI’s role in the dental field.

Data extraction

Initially, we identified a total of 73 articles. After screening, we ultimately included a total of 56 articles from the years 2018 to 2024. During the final selection process, we prioritized studies that were timely, relevant, and methodologically sound. We excluded articles that were too old, not valid or relevant, or had largely redundant or irrelevant data. This selection process greatly aided in ensuring that we used credible evidence and not poor evidence for our analysis, which served as an evidence-based approach to understanding the current state of AI in dental education. By collating the findings of those articles we selected, we presented a picture of the positive and negative implications of AI in dental education, as well as recommendations for future applications and research [Figure 1].

Figure 1.

Our study flowchart

Discussion

AI in dentistry

Dental professionals are increasingly aware of the value of AI in their learning processes by enhancing faster diagnosis, clinically relevant, high-quality data at their fingertips, and fewer mistakes.[12] AI is a key diagnostic aid, especially in detecting subtle and often-missed dental caries. Mainly, dental radiographs, caries detectors, and visual inspections are the means for caries detection in dental practice. AI can supplement these modalities by finding caries that traditional approaches are likely to miss. For example, U-shaped CNNs, a type of DL framework, have shown better caries diagnosis accuracy from bitewing radiographs.[13] Recently, a systematic review found that DL models had lower accuracy for detecting caries than traditional methods, including periapical and bitewing radiographs and near-infrared transillumination imaging.[14] This suggests that AI should be a complement, not a substitute, to human knowledge in dental practice. AI can support professional judgment and decisions, yet it has not yet achieved the accuracy or even complexity to perform multivariate diagnostics to human professionals.[15]

AI has also been shown to aid in the diagnosis of radiographic periapical lesions, showcasing a potential avenue of utility in dental diagnosis.[16] In terms of virtual dental implant planning, AI-based tools such as 3D U-networks have been used to place dental implants adequately relative to anatomical landmarks. The platform essentially outperformed human experts over two times faster to use and has outperformed human-planned implants on certain assessments (Turing tests).[3] These procedures exemplify the potential of AI to drastically improve diagnosis and/or clinical performance while showcasing the need for humans’ continued role in monitoring an appropriate clinical response and diagnosis.

Applications in periodontics and endodontics

CNNs have been a helpful tool for diagnosing some conditions in periodontics, such as gingivitis, periodontitis, bone loss, and inflammation.[4] These AI systems can efficiently process clinical images and radiographs, offering practicing professionals a more accurate means of treating periodontal disease in its earlier stages. This leads to fewer complications from misdiagnosing periodontal disease; in turn, patients ultimately experience more favorable outcomes and care when presented with the right diagnosis. Similarly, in endodontics, AI models have been utilized mainly to assist in the identification of periapical lesions and root canal length estimation using ResNet-based DL models in dentistry. Determining root canal length is critical for the accurate planning of treatment options and the success of treatment. AI allows for the often tedious and complex process of dental image interpretation to be streamlined, presenting efficiencies and opportunities for more accurate diagnoses.[8] Overall, AI/ML technology improves the interpretation of dental images by simplifying the process and empowering professional decision-making within periodontics and endodontics, ultimately helping the patient and their treatment.[17] The future of these technologies has great potential as they develop, which may present additional applications and possibilities in the field of dentistry.

Prosthodontics

AI has taken a firm hold on the field of prosthodontics, with the use of CAD/CAM and AI enhancing the precision of designs for crowns, bridges, and dentures. Using AI algorithms, CAD/CAM systems can examine patient-specific anatomical data to create garments that custom-fit with the best possible accuracy to the patient’s anatomy and are also more comfortable. In addition, they are taking advantage of generative adversarial networks (GANs) to design prosthetic components that are biomimetically accurate. This can make the prosthetic components made to closely replicate the natural look and function of teeth for a better aesthetic and patient satisfaction. GANs are also creating a more efficient process of design. Overall, the use of AI in prosthodontics improves the design and manufacturing processes, as well as patient treatment, thereby enhancing the quality of care. As these technologies develop, there is potential to advance prosthodontic practice to provide customized solutions for individual patients.[9]

Oral and maxillofacial radiology

Technology advancements have greatly changed the practice of oral and maxillofacial radiology. One specific use of AI has shown promise with the use of deep CNNs that detect the first signs of osteoporosis on dental panoramic images. This common use of AI can alert the practitioner to a possible pitfall in the patient’s future due to bone density concerns, essential information for preventive care or treatment planning. Within oral and maxillofacial radiology, this new technology draws from earlier studies of apical lesions and root caries to offer an enhanced method of using AI as a new instrument to assist the practitioner in detecting abnormalities in radiography or imaging.[18,19] In addition, AI has used the same premise to great success in identifying extranodal extensions of cervical lymph node metastases in CT imaging.[20] In many scenarios, AI surpassed the capabilities of professional radiologists in this setting and allowed for an assessment that was faster and also marked with a greater speed of accuracy. This is a major finding as it could demonstrate efficacy for the interpretations and diagnostic assessments performed by professional radiology in complex cases.[21] Further proof of concept has established that the use of AI technology as a means of practice in oral and maxillofacial radiology is reinventing the way dental professionals conduct diagnosis and elevating their assessments in a more thorough and efficient manner, promoting better clinical decisions. As this technology evolves, it will continue to push the boundaries of radiology to executive assessments of the highest standard in dentistry.

Orthodontics

Regarding orthodontics, deep convolutional networks (DCNs) are quite significant in helping with cephalometric analysis studies, which assist in treatment predictions. As demonstrated in previous research, DCNs, when trained on cephalometric radiographs, allow AI to learn craniofacial relationships and aid professionals in establishing treatment plans.[10] AI also allows for the creation of 3D models and dental structures that explore and plan treatment. Creating 3D models helps mentally conceptualize possible treatment outcomes while planning a treatment, allowing for better treatment decisions based on individual patient needs. In addition, AI has begun to be used to evaluate tooth extractions for orthodontic treatment via lateral cephalometric data. Exploring the diagnostic applications of AI in Orthodontics leads practitioners to articulate complex cases and treatment plans even more effectively, now that there is digital assistance in the diagnosis.[22] In general, the utilization of AI technologies to help facilitate diagnostic procedures in orthopedics also enhances the efficiency to help increase the accuracy of a (orthodontic) diagnosis subsequently. As AI technology continues to grow, the opportunities to use AI will follow suit.

Oral pathology and cancer detection

AI models such as CNNs and support vector machines (SVMs) have better driven pathology and cancer detection technology and support better diagnostic outcomes. AI technology has been utilized to identify and classify oral cancers using examinations, imaging, and cytological analysis. AI has been used to diagnose with high precision and to detect malignant lesions earlier.[11] AI-driven models build a strong case for evaluating cancerous lesions, as they allow for earlier detection within lesions with possible cancer worsening the patients’ condition.[11] AI models in pathology may raise the volume of treatment success while enhancing the overall quality of care. AI technology has advanced the body of knowledge and practice while creating efficiencies and supporting pathologists’ decisions as they diagnose conditions for patients.[23] As AI technology continues to develop, the uses and applications for cancer detection based on BOC will only expand further with more advanced AI-driven technologies used by health practitioners to fight oral cancers [Table 2].

Table 2.

Important results on the use of AI in a range of dental applications

Application Area		Key Findings
General AI in Dentistry		Enhances diagnostic accuracy, reduces errors, and supports faster diagnoses.
Caries Detection		AI tools like U-shaped CNN improve caries detection precision but still require human oversight.
Periodontics		CNNs effectively diagnose gingivitis, periodontitis, and bone loss, facilitating early intervention.
Endodontics		AI models determine root canal length and identify periapical lesions, enhancing treatment planning.
Prosthodontics		AI in CAD/CAM systems improves the design of crowns and dentures, leading to better patient satisfaction.
Oral and Maxillofacial Radiology		Deep CNNs detect osteoporosis and outperform radiologists in specific diagnostic tasks.
Orthodontics		DCNs aid in cephalometric analysis and treatment outcome predictions, enhancing planning precision.
Oral Pathology		AI models detect and classify oral cancers, improving early detection and patient care outcomes.

AI = Artificial intelligence, CNN = Convolutional neural networks, CAD/CAM = Computer-Aided Design / Computer-Aided Manufacturing, DCNs = Deep convolutional networks. The important findings related to the application of AI in dentistry’s various fields are compiled in this table, which also emphasizes how it affects patient outcomes, treatment planning, and diagnostic precision

Advantages of integrating AI in dental education

The incorporation of AI into dental education offers several benefits to educators, including time savings by automating administrative tasks, greater diagnostic accuracy, and simplified clinic procedures, and learning processes with a strong adaptive personalized learning component where tasks are configured to meet individual student needs. AI also supports increased engagement from patients with efficacious targeted interventions, unique data analysis for research purposes, generating ideas, connections with multilingual materials for international collaboration, and the advantage of increased creativity and innovation from students, providing a valuable experience allowing flexibility in and outside of the classroom. AI is an emerging technological advancement in the fields of medical and dental sciences. It was found that most dental professionals possess strong knowledge of AI and support its integration into educational curricula and clinical practice.[24]

AI can be incorporated into dental education in a number of ways. Here are a few potential examples: integrating AI-powered decision support systems into clinical education to teach students how to make well-informed treatment decisions; using AI-powered image analysis and interpretation technologies to enhance students’ comprehension of radiographic images; and using AI-powered simulation tools to give students practical experience in the diagnosis and treatment of dental disease. AI may also increase the accuracy and efficiency of documentation and administrative duties such as appointment scheduling and patient record management. These are but a handful of the ways AI might be incorporated into dental school curricula.

Time efficiency

The integration of AI into dental education will lessen the burden on professors and the time and energy required to deliver effective teaching, allowing them to spend more energy on teaching delivery and student engagement rather than grading, paperwork, and scheduling. This will lead to a better academic experience for students and a better educational experience for faculty. Nonetheless, it is educationally important for universities to keep up with advances in technology; as they fall behind, they risk losing sight of their educational goals and under-preparing students for a continuously developing dental profession. Successful utilization of AI in dental education requires a comprehensive infrastructure of advanced educational technology/resources and support network.[25] Institutions must invest in employees who can be trained to fully utilize these new AI technologies, allowing both faculty and students to leverage their full potential. Moreover, institutions must also provide the necessary resources, training, and ongoing support that transition students and faculty into an AI-augmented learning environment.[26,27] AI will assist in diagnosis accuracy, clinical procedures, and affect treatment outcomes.[28] Integrating these technologies into the curriculum will help dental education effectively equip students with the skills needed to deal with the complexities of current dental practice. New courses would be directly related to AI and its applications in Dentistry, preparing students for a new era of practice where technology and patient care will intermingle.

Increased patient engagement

As facilitators of healthcare, we use care plans to manage chronic or acute conditions. The success of care plans is entirely dependent on the adherence of patients to their care plans. Noncompliance (failure to follow prescribed treatments or implement necessary behavior changes such as filling prescriptions or having follow-up appointments) is a major barrier to effective healthcare delivery. AI and big data analytics can provide us with new ways to increase adherence and engagement. AI can help to personalize interventions and support patients in real-time and could also do this in a way that targets specific patient behavior and needs. AI can help make patient care easier for patients to understand and adhere to by providing them with targeted interventions in a real-time fashion. Targeted AI interventions can also empower patients and provide a better relationship with their healthcare provider, which is associated with a higher level of satisfaction with the healthcare experience. AI systems have the opportunity to return the focus of a clinician back to the patient and eliminate the time-consuming aspects of administrative follow-up (paying attention and reminding sometimes driven by regulations). This gives our medical staff relief from stress and is another way that we all can focus our efforts to provide quality care and allow patients to focus on building a meaningful relationship with their healthcare providers. All of this can lead to more positive treatment outcomes and allow patients to embed themselves more thoroughly in their health management.[27,29]

Research idea generation

AI tools, including ChatGPT, are an important source of support for researchers in a variety of ways, including their role in supporting data analysis and interpretation, as well as scenario generation and effective communication of findings. These AI tools can mine huge amounts of information quickly and thus help researchers detect patterns and infer insights that might not have been observable otherwise. By providing access to large amounts of data, AI can engage researchers around new ideas or new ways to approach existing problems. However, despite the many benefits of AI tools, it is important to acknowledge that they never replace the rigor or nuance that engages human interpretation.[30,31] AI can consider data and make suggestions for potential avenues of exploration, but it cannot replace a human being’s (the researcher’s) critical thought, insightfulness, and contextual relevance. Researchers must still bring their experiences and creativity to research, with application of AI pre-eminently supporting the practice. If researchers use AI as just a tool to support their role, they will enhance their productivity and creativity through the process of engaging with the AI and ultimately delivering innovative and impactful research.

Multilanguage assistance

AI technology is facilitating access to multilingual resources so that researchers can work together across languages. AI tools improve academic writing in multilanguage, international research contexts by identifying translational mistakes. For example, ChatGPT can assist users in developing language skills through machine translation and make sure complex ideas are correctly and effectively transmitted. This not only helps to eliminate a language barrier but, in some cases, it also increases access to important information for a range of audiences.[32,33] As a result, researchers from varied backgrounds can become increasingly engaged in collaborative projects, facilitate knowledge sharing, and contribute to a more intense academic conversation. By improving communication, AI enables and fosters increased inclusion in research environments where insights and innovations can materialize from diverse understandings and expertise.

Creativity and innovation

Advancements in ML are actively fostering creativity and opening up entrepreneurial opportunities for students in various fields. By providing tools that enhance creative processes, AI encourages innovative thinking and problem-solving, empowering students to explore new ideas and ventures. However, the rise of AI also brings potential shifts in the workforce, as some administrative and support roles may be replaced by automation.[34] Despite these changes, AI enriches teaching and learning experiences, transforming how education is delivered and experienced. By incorporating AI into the classroom, educators can create dynamic and engaging learning environments that stimulate creativity and collaboration among students. This integration not only prepares students for future careers in a technology-driven landscape but also inspires a culture of innovation that can lead to groundbreaking developments in various disciplines [Figure 2].[35,36]

Figure 2.

Advantages of integrating artificial intelligence in dental education. The main advantages of integrating AI into dental education are listed in this figure, along with how it will improve patient engagement, teaching effectiveness, diagnostic accuracy, research support, and student creativity

Limitations

Although AI has demonstrated potential in clinical decision-making, it is essential to acknowledge that it is not yet a complete substitute for human judgment, and it has several limitations that must be recognized.

Misunderstandings

Both students and instructors expressed serious concerns over misunderstandings and privacy issues from excessive data collection and due to these tools frequently relying on gathering a sizable amount of data to improve their functions and the learning experience.[37,38] Poor data collection can raise fears of how their data will be used, stored, and shared. Additionally, students can misunderstand the AI-generated feedback or recommendations, sometimes creating confusion as it relates to their learning process. Therefore, inviting instructor involvement to provide realistic feedback and guide effective decision-making is paramount. Instructors can explain AI outputs and help teach students how to navigate these tools, while also dispelling any misconceptions and misunderstandings about them. Without appropriate control and communication, the risks of miscommunication and data misuse can happen, negating every potential benefit to enhance the educational experience and acting as a barrier to effective and trustworthy learning impacted by AI technologies.

Plagiarism

The emergence of advanced AI technologies such as ChatGPT raises good ethical questions about plagiarism and data ownership. Plagiarism is not merely copying text; it is a wide-ranging violation of someone else’s intellectual property, which includes reworded and paraphrased content, ideas, graphics, and intellectual property of any kind. Plagiarism becomes a bigger concern with copyright issues, as the laws around AI-generated content and data are being formed. There are growing worries that students will use AI-generated analysis and submit previously created material that does not reflect their work or original ideas, which would ultimately harm their reputation and standing. Problems with AI tools in journalism have already arisen when ethical lines have been crossed with regard to plagiarism; this already highlights the need for clarified guidelines with respect to plagiarism and the ethical use of AI-generated content in higher education institutes.[30,39] Higher education is a prime situation to advocate for a culture of transparency and academic honesty and limit the potential for students to misuse AI. With a clear approach to the ethical usage of AI-generated content, it is possible that higher learning institutions could be more meaningfully involved in students using AI.

Dataset shift

AI performance is highly dependent on the training datasets; therefore, even slight changes in training circumstances can result in a large shift in performance. The training datasets need to be truly representative of the situation in the real world; if they are not, there can be significant mismatches that produce faults.[40,41] For example, if an AI model is trained predominantly from deidentified data from one demographic, it may not perform well when applied to others. This reiterates the necessity for continuing evaluation and updates to training datasets, particularly when new data is generated, or social standards or practices have changed from when the original training dataset was established. Institutions must establish robust methods for monitoring AI performance, making changes, and preventing bias.

Bias and inaccuracy

While the potential of AI to revolutionize learning is recognized, current tools such as ChatGPT have been acknowledged in the study to possess limitations, including factual inaccuracies and inherent bias.[42] AI algorithms trained on datasets that are not comprehensive and representative for specific groups may yield biased and erroneous results, as these models will not necessarily identify the variations of different populations. For instance, if an AI system is developed primarily using data from only one demographic group, the system may not consider the needs of other groups and yield biased results. This can impact many areas, especially in healthcare and education, where biased outcomes can aggravate issues of inequality. As such, AI systems may reflect biases or errors from the data they are trained on, making accuracy validation much more complex.[43] To ensure ethical and fairness integrity of AI systems, investigating bias is an important first step. This requires institutions to prioritize diversity in the datasets they are using, particularly for algorithms intended to represent and impact any demographic groups. Institutions should also consider implementing a strategy to systematically audit AI model outcomes for bias, to understand whether an AI model is fair or equitable for all users.

Overreliance

AI can be an effective adjunct to traditional modes of learning; however, students should not forgo the ability to think critically or develop problem-solving skills. Reliance on AI can ultimately hinder the development of critical thinking and problem-solving skills, particularly in situations where students need to make immediate decisions.[44] For example, dental students who have become reliant on using AI as diagnostic support may be less able to analyze their cases or make decisions on their own when they are no longer reliant on AI. This is especially important in healthcare fields where practitioners may need to act without the advantage of time, as diminished decision-making skills can impact patient care and outcomes. For this reason, we feel it is especially important for educators to clarify the importance of developing critical thinking and the ability to solve problems in their particular field, with a focus on the use of AI tools and the associated practice. In this way, students can take advantage of AI in their learning without losing the analytical skills necessary for practice.

Expertise gaps

Many of these AI tools rely on non-domain-specific skills, which require the careful judgment of the user and assessing AI-generated outputs in terms of dependability and consistency. While AI can assist with data analytics and decision-making skillsets, AI inevitably lacks specialist understanding aspects, such as when working in dentistry,[45] not providing the nuanced understanding necessary. Treatment planning and case-related decisions require knowledge cultivated over time through cumulative interdisciplinary study and critical thinking. Presently, these AI tools cannot provide the level of expertise needed to place full reliance on them in terms of treatment planning, indicating that full human authority and responsibility are still required in these critical practice aspects. Institutions must focus on training their students about critically evaluating recommendations provided through AI and developing competencies to enhance and synthesize their learning using the IA tools. Institutions may commingle the idea of engaging AI tools to support a collectively intelligent response; based on student research, they may be able to adopt their knowledge while engaging the AI’s benefits. I would argue that Theoretical Institute-related programs will gift students the opportunity to think about how to engage in multidisciplinary, interdisciplinary, and transdisciplinary best educational practices, with leveraging technological AI tools to help in authentic learning areas.

Ethical considerations and global impact

From an academic and professional perspective, it is necessary to consider the ethical issues surrounding the use of AI in dentistry to responsibly and knowledgeably employ these tools.[46] Major issues, such as data privacy, informed consent, and regulations that shape these areas, need to be examined closely.[47] In the dental landscape, the use of AI almost always involves gathering and examining sensitive patient data. As educators and practitioners, we have a responsibility to know whether the data is being properly protected. As with other sensitive data, we need to abide by regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), as protection of patient data must remain a priority.[48,49,50] The educational programs we set up must instruct the students on the importance of regulations and guide students on not only the technological aspects of AI but also the ethical considerations that surround the use of sensitive information. We hope that the students establish a strong foundation in data privacy, which will help them use AI responsibly. Informed consent is another pillar of ethical responsibility that should be the focus of educational programs when employing AI technologies in dental care.[51] Patients need to have complete transparency regarding how their data will be used and what role AI plays in their care plan. It is our responsibility as educators to develop the communication skills of our students to communicate AI’s involvement clearly and openly. This will help to build trust with patients, fostering patient autonomy in treatment decision-making. By urging patients to engage with us in discussions about AI, we can demystify AI’s capabilities and limitations and help patients make informed and empowered choices about their care.[52] We know that the regulatory landscape surrounding AI in healthcare is shifting, and it is important that students and practitioners stay informed and aware. We encourage that an agenda of conversation about changes in AI technology include diverse stakeholders, including, but not limited to, politicians, practitioners, and ourselves as educators, to create an expectation of comprehensive guidelines for ethical and safe use of AI technologies. Stimulating this conversation with students about a possible regulatory framework that may emerge would stimulate student engagement with the narrative of AI in dentistry, that situates care within safety and ethical practice.

Global impact and low-resource settings

AI has the potential to reduce major global health inequities by offering innovative solutions to problems faced by underserved populations.[53] For example, AI-enabled diagnostic devices can improve access to dental care in remote and low-income areas where specialists are hard to find.[54,55] AI can improve a healthcare professional’s workflow. By automating routine tasks, AI can allow healthcare professionals to spend less time on non-patient care and administrative activities. Further, the adoption of AI by local healthcare providers can be successful, and they can ultimately establish conditions that may lead to better patient outcomes.[55] As educator learners, we need to point out that AI technologies should be thoughtfully designed to support culturally competent care of diverse populations. In addition to broadening dental education’s global focus, focusing on global issues represents our collective ethical responsibility to ethically leverage new technologies for their social good across all populations, especially those that need it most.[56] As we integrate AI into dental education and practice, it is important to always focus on the ethical issues and global implications of the use of these technologies. If we prepare our students to grapple with these issues, we can create a generation of practitioners who embrace future technologies, but do so in a principled and egalitarian manner.

Future prospects

As derived from our study, the conclusion is to recommend that educational institutions implement courses based on AI and AI’s different functions in the dental field, including fundamental concepts, practical applications, and ethical considerations in dentistry. With this, students learn the skills and knowledge for using AI in their practices in the future. Faculty will require ongoing professional development and training so they can provide students with the most relevant and current information. Considering the limits of AI and the possible threat to dental students’ decision-making, it is important to understand the important role instructors play in directing students toward effective use of AI, while helping them navigate a world of AI tools and ensuring they are conscious of inaccuracies and biases. One viable approach could be consistent reflection on errors, as well as on the content produced by AI systems. Educators can initiate student dialogue about errors, thus establishing a form of professional practice that emphasizes thinking and analysis, reducing the risks associated with AI, at the same time allowing students to make informed decisions about using these AI systems. Furthermore, encouraging discussion about the limitations and ethical implications of these statements and systems will influence how students utilize these modalities in their careers.

Conclusion

AI is essential to the development of dentistry and has the potential to greatly improve patient care and dental education. To fully utilize AI’s advantages while addressing its inherent drawbacks, dental curricula must incorporate it. Educational institutions can guarantee that future practitioners possess not only technical skills but also ethical considerations and regulatory knowledge by coordinating AI training with health policy benchmarks. Students who receive the right instruction and direction will be able to use AI efficiently, which will enhance patient care and learning outcomes in the long run. It is important to recognize that these AI models have limitations; the quality of the data, the clinical settings, and the particular AI tools used can all affect how well the models perform. These difficulties can be lessened by implementing a policy-driven strategy.

Abbreviations

AI: Artificial Intelligence; ANN: Artificial Neural Network; CNN: Convolutional Neural Networks; DL: Deep Learning; ML: Machine Learning; NN: Neural Networks.

Author’s contributions

ZK: Project Administration, Methodology, Writing – original draft. SG: Conceptualization, Investigation, Writing – Review and Editing. AA: Formal analysis, methodology, writing – original draft.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.