Stoma Edu J. 2024;11(1-2):
pISSN 2360-2406; eISSN 2502-0285
www.stomaeduj.com
Articial intelligence (AI) holds immense promise in revolutionizing dentistry, oering a wide range of
applications in educational tools, diagnostics, and treatment planning. This is a brand new frontier with
exciting challenges! In this editorial we will outline the role of AI in dentistry, its potential applications, the
challenges it faces, and strategies to overcome them [1].
A major nexus where AI-driven technologies can make a signicant impact is dental education [2]. AI can
provide personalized learning experiences and simulation-based training, enhancing the educational
processes for aspiring dentists as well as for those in dental practice, to horn their skills. By leveraging AI,
dental students can receive tailored instruction and practice in a virtual environment, improving their skills
and knowledge. Both the theory and practice of dental pedagogy in now at a cusp of this AI revolution.
In clinical practice, AI can assist in a myriad ways. For instance, the early detection of bone loss through
advanced image analysis techniques. By analysing radiographic images, AI algorithms can identify subtle
changes in bone density, not decipherable by naked eye examination, enabling timely intervention and
improved patient outcomes [3]. This capability is particularly valuable in the eld of periodontology, where
early detection of bone loss is crucial for successful therapy.
AI also shows promise in oral cancer diagnosis. By analysing digital images and histological data [4], AI
algorithms can aid in the detection and classication of oral lesions. This technology can assist dentists in
identifying potentially malignant or cancerous growths, leading to earlier interventions and improved
prognosis for patients.
In restorative dentistry, AI can enhance diagnostic accuracy and treatment planning. AI algorithms can detect
conditions such as vertical root fractures and dental caries by analysing dental images and patient data
[2]. Furthermore, AI-driven approaches in prosthodontics enable the customization of dental crowns and
restoration designs [5]. By leveraging AI, dentists can create highly personalized dental prosthetics that t
patients' unique anatomical features and functional requirements. This optimization of dental restorations
enhances patient care and treatment outcomes.
Despite the potential of AI integration in dentistry, several challenges need to be addressed. Data availability
is a signicant concern, as AI algorithms require large and diverse datasets to achieve optimal performance[6].
Dentistry needs standardized and comprehensive datasets to train AI models eectively. However, in the
fullness of time, with increasing data inputs, and the consequent expansion of the databases, the specicity
and the sensitivity of AI diagnostics will continue to improve.
There is a slang aphorism known as "garbage in leads to garbage out", and this aphorism perfectly applies to
AI technology. There are several common examples of "garbage input" that can result in poor AI performance,
as follows:
I. Insucient or biased training data: If the dataset used to train an AI model is incomplete,
unrepresentative, or biased, it can lead to inaccurate or unfair results. For example, if a facial recognition
system is trained primarily on data from a specic demographic, it may struggle to accurately identify
individuals from underrepresented groups.
II. Noisy or corrupted data: When the input data contain errors, inconsistencies, or irrelevant
information, they can adversely aect AI performance. Noisy data can confuse the model and lead to incorrect
predictions or outputs.
III. Lack of diversity in training data: AI models benet from diverse training data that encompass
various demographics, backgrounds, and perspectives. Lack of diversity can limit the model's ability to
generalize and perform well in real-world scenarios that involve dierent populations.
IV. Inadequate data preprocessing: Preprocessing is an essential step in preparing data for AI models.
If data preprocessing techniques like cleaning, normalization, or feature extraction are not applied properly,
they can introduce errors or distortions that impact the model's performance negatively.
V. Overtting or undertting: Overtting occurs when an AI model becomes too specialized in the
training data, leading to poor generalization to new, unseen data. Undertting, on the other hand, happens
when the model fails to capture the underlying patterns in the training data, resulting in suboptimal
performance.
Artificial Intelligence: The New Frontier of Dentistry
Guest Editorial
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Lakshman SAMARANAYAKE
BDS, DDS, FRCPath
FHKCPath, FCDSHK, MIBiol
Faculty of Dentistry
University of Hong Kong
Nozimjon TUYGUNOV
BSc, MDSc
Faculty of Dentistry
University of Hong Kong
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Stoma Edu J. 2024;11(1-2):
pISSN 2360-2406; eISSN 2502-0285
www.stomaeduj.com
Guest Editorial
VI. Lack of context or relevant features: AI models rely on contextual information and relevant
features to make accurate predictions. If important contextual cues or relevant features are missing from the
input data, the model may struggle to understand the problem or produce meaningful outputs.
Addressing these issues and ensuring the quality, diversity, and representativeness of the input data is crucial
for obtaining reliable and high-performing AI systems [7].
Privacy concerns also arise when implementing AI in dentistry, as patient data must be handled securely and
in compliance with relevant local and regional regulations [8]. Strict data protection measures should be in
place to safeguard patient condentiality and privacy.
To overcome these challenges, several strategies can be implemented. First, fostering new perceptions of AI
within the dental community is essential. Education and awareness programs can help dental professionals
understand the benets and limitations of AI, encouraging its adoption [8]. Next, setting clear objectives for
AI integration and aligning them with the needs of dental practice is crucial. Identifying specic areas where
AI can have the most signicant impact and dening measurable goals will help guide the implementation
process. Finally, cultivating a supportive work culture that encourages AI technology is also important. Dental
professionals should embrace AI as a tool that enhances their practice rather than a threat for their practice
and expertise. Training programs can help develop the necessary skills to utilize AI eectively.
Though investments are required to implement AI in dentistry, they should be thoughtful and targeted.
Collaboration between academia, industry, and dental institutions can facilitate the development of AI
technologies and their integration into dental practice. Regulatory frameworks need to be established to
ensure the ethical and responsible use of AI in dentistry. Guidelines should be developed to address issues
such as data privacy, algorithmic transparency, and accountability [9].
Above all, advancing AI literacy among dental professionals is crucial. Continued education and training
programs should be provided to enhance the understanding of AI concepts and applications, enabling
dentists to make informed decisions regarding the use of AI technologies [7].
By addressing these challenges and implementing the proposed strategies, AI has the potential to revolutionize
dental care. It can improve patient outcomes, drive innovation, and transform the clinical practice of dentistry
into a more ecient and eective healthcare discipline. The time to embrace this challenge is now!
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