Introduction
Artificial intelligence (AI) has already begun to revolutionize healthcare, offering unprecedented advancements in diagnosis, treatment planning, and patient care.1 While disciplines such as radiology and oncology have seen significant integration of AI, dentistry remains a field where its potential is yet to be fully realized. This disparity highlights a critical gap: the absence of specialized Ph.D. programs in AI applications for dentistry in the United States. As AI continues to advance, there is a pressing need to train dentists who are proficient not only in clinical practices but also in digital dentistry and AI technologies.
State of AI in dentistry
AI applications in healthcare have shown to be effective and beneficial, enabling dentists to achieve greater accuracy in diagnostics and clinical decision-making.2,3 Digital dentistry, which includes digital imaging and computer-aided design and manufacturing (CAD/CAM), is transforming dental practices by improving the efficiency and accuracy of treatments. AI further enhances these technologies by enabling auto-designing of dental restoratives such as crowns and laminates and optimizing implant positioning through sophisticated algorithms. Beyond these applications, AI can significantly contribute to formulating comprehensive and customized treatment plans. By analysing vast datasets, AI can help dentists predict patient outcomes, manage treatment risks and tailor interventions to individual needs. A summary of how AI can be integrated into dentistry is shown in the Table 1 and Figure 1.
Table 1.
Different application of AI in dentistry.
| Dental specialty | Applications of AI |
|---|---|
| General dentistry | - Automated Patient Management: Streamlining scheduling and data management. - Quality Control: Analysing treatment quality. - Enhanced Customer Service: Providing AI-powered assistance and advice. |
| Diagnostic radiology | - Diagnostic Imaging: Analysing dental X-rays, CT scans, and MRI images for pathologies. - Erosion and Wear Analysis: Monitoring tooth wear and erosion. |
| Oral pathology | - Disease Diagnosis: Utilizing algorithms to diagnose diseases such as oral cancer and periodontitis. - Early Detection of Systemic Health Issues: Identifying signs of systemic diseases. |
| Orthodontics | - Treatment Planning: Designing and managing orthodontic treatments. - Predictive Analytics: Predicting treatment outcomes and adjustments. |
| Restorative dentistry | - Restorative Dentistry: Aiding in the design and creation of dental prostheses. - Material Selection: Choosing optimal materials based on aesthetics and patient compatibility. |
| Periodontics | - Periodontics: Monitoring gum health and disease progression. - Predictive Analytics: Predicting future periodontal issues for preventive care. |
| Endodontics | - Endodontics: Assisting in root canal treatments by identifying canal anatomy and potential complications. - Anaesthetic Management: Optimizing the delivery and response to anaesthesia. |
| Oral surgery | - Oral and Maxillofacial Surgery: Enhancing surgical precision through pre-surgical planning and intraoperative guidance. - Postoperative Monitoring: Tracking recovery and intervention needs remotely. |
| Prosthodontics | - Customized Treatment Devices: Designing devices like night guards and retainers. - Restorative Dentistry: Creating precise and aesthetic dental prostheses like crowns and bridges. |
| Teledentistry | - Teledentistry: Supporting remote dental assessments and consultations to increase accessibility. - Patient Education: Offering personalized educational content remotely. |
| Preventive dentistry | - Risk Assessment: Analysing genetic, lifestyle and clinical data for preventive strategies. - Predictive Analytics: Using historical data to predict and prevent future dental issues. |
| Environmental management | - Environmental Impact Reduction: Optimizing waste management and energy usage in dental practices. |
| Research and development | - Drug Discovery and Pharmacology: Accelerating the development of new dental medications. - Collaborative Care: Facilitating integrated care through shared insights and data across healthcare disciplines. |
Fig. 1.
AI application in dentistry.
Need for specialized education
Despite these advancements, the U.S. lacks special postgraduate programs (e.g. PhD or Masters) specifically focusing on AI applications in dentistry. This educational gap hinders the full integration of AI into dental practices and limits the development of new technologies tailored to the unique needs of dental care. A systematic review showed that the average basic AI knowledge score among dental students was 58.62%.4 There is a compelling need for academic programs that not only teach the fundamentals of dental medicine but also delve deeply into AI's potential applications in this field. Establishing such programs would equip future dentists with the necessary skills to navigate the evolving landscape of digital health technologies and improve patient care through innovative solutions.
Case studies and examples
The transformative impact of AI is evident in several medical fields. For instance, in oncology, AI algorithms analyse imaging data to detect anomalies that are imperceptible to the human eye, enabling early diagnosis and treatment of cancers.5 Similar AI applications in dentistry could revolutionize diagnostics and preventive care, reducing the incidence of common dental issues and enhancing the efficacy of treatments. Furthermore, academic initiatives like Stanford University's AI in Healthcare specialization demonstrate the viability and success of integrating AI education within medical fields, suggesting a promising model for dental education.
Proposed educational framework
To cultivate expertise in AI within dentistry, academic institutions must develop a comprehensive educational framework that includes:
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Core courses on AI and Machine Learning: These courses should cover basic and advanced concepts in AI, including data handling, machine learning models and their application in medical imaging and diagnostics.
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Hands-on training with AI tools: Practical sessions using AI-powered diagnostic tools, treatment planning software and digital design programs should be integral to the curriculum.
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Interdisciplinary collaboration: Dental schools should collaborate with engineering departments and AI research centres to provide interdisciplinary learning experiences and drive innovation in dental AI applications.
Challenges and considerations
Introducing AI into dental education poses several challenges. Firstly, significant investment is required to develop infrastructure, acquire modern equipment, and train faculty. There is also the need to continuously update the curriculum to keep pace with rapid technological advancements. Ethical considerations, particularly concerning patient data privacy and the reliability of AI decisions in clinical settings, must be rigorously addressed. Faculty and practitioners must be prepared to navigate these issues, ensuring that AI is used responsibly and effectively.
Call to action
To overcome these hurdles and harness the potential of AI in dentistry, a concerted effort from educational institutions, policymakers, and industry leaders is essential. Dental schools should prioritize the development of AI-focused curricula and seek partnerships with technology companies to facilitate state-of-the-art education and research. Professional associations should advocate for standards and guidelines that govern the ethical use of AI in dental practice.
Conclusion
The integration of AI technologies into dental education is not just an opportunity—it is an imperative for advancing the field of dentistry. By developing specialized programs and incorporating comprehensive AI training into dental curricula, the U.S. can lead in promoting innovative, effective and efficient dental care. It is time for academic institutions to embrace this change and prepare the next generation of dentists to be as skilled in digital dentistry as they are in traditional dental medicine.
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
Ethical approval
There is no ethical issue.
Author contributions
M. D.: Conception and design of study, Acquisition of data, Drafting of article and/or critical revision, Final approval of manuscript.
Sh. Gh.: Acquisition of data, Analysis of data, Drafting of article and/or critical revision, Final approval of manuscript.
Z. K.: Conception and design of study, Analysis of data, Drafting of article and/or critical revision, Final approval of manuscript.
All authors have critically reviewed and approved the final draft and are responsible for the content and similarity index of the manuscript.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.
References
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