Artificial intelligence (AI), a branch of computer science, is a fast-growing field

in healthcare. This term was first introduced in 1956 at Dartmouth University [1].
By definition, it is “the theory and development of computer systems able to
perform tasks normally requiring human intelligence, such as visual perception,
speech recognition, decision making, and translation between languages” [2]. The
major subfields of AI include machine learning (ML), deep learning (DL), artificial
neural networks (ANNs), and robotics [3]. In dentistry, ML and its subset DL have
found a major role in diagnosis, decision-making, predicting treatment outcomes,
and treatment planning.
ANNs consist of an input layer, an output layer, and multiple small communicating
units called neurons, which are organized in layers. Convolutional neural networks
(CNNs), a subset of an ANN, are commonly used in medicine and dentistry. By using a
mathematical operation called convolution and a special neuron architecture, a CNN
processes the input data [4]. In dentistry, the input data can mainly be in the
form of text (case records, laboratory reports), images (clinical images, x-rays),
or sounds (handpiece sounds). A CNN uses filters to scan small amounts of data at a
time. These filters, also known as kernels, help the network detect patterns and
features at different spatial scales. The pooling layers then reduce the complexity
of the network, making it stronger to the variations in the object position and
scale [5]. AI generally operates in two phases - the training phase and the testing
phase. In the training phase, the AI model learns the features, relationships, and
patterns of the data. The goal of this phase is to teach the model to make accurate
predictions or decisions. In the testing phase, the trained AI model gives
predictions or decisions for new data that it has not encountered during the
training phase [6].

Implant dentistry has advanced exponentially all over the globe over the last few
decades. Implantology has changed the face of dentistry, especially for the
rehabilitation of patients with single, partial, or complete edentulism. There have
been enough literature reports on the improvements in the patient’s overall quality
of life following implant treatment [7-8]. Continued research and advanced
technologies have improved the success and survival rates of implants. The
advancements in this field have, however, come with their share of complications,
such as peri-implantitis, prosthetic complications, and issues with super-
structures or the implant. Managing implant complications is a challenging task, as
highlighted by Hanif et al. [9]. This narrative analysis examines the prevailing
literature, emphasizing the integration and implications of AI in dental
implantology. A pivotal inquiry guiding our review is "In what ways is AI
influencing dental implantology across areas such as treatment planning, implant
brand distinction, design innovation, outcome prediction, and robotic-assisted
surgeries?" Through this detailed exploration, we underscore AI's increasing
significance in refining the domain of implant dentistry.
Applications of AI to implant dentistry
Assist in Treatment Planning
Cone beam CT (CBCT) scans are the gold standard for dental implant planning all
over the world. General dental practitioners may not have the necessary skill sets
to evaluate CBCT scans for detailed implant planning and identification of
anatomical structures. AI can contribute to solving this problem. Bayrakdar et al.
used DL for dental implant planning in CBCT images and noted limited success with
the same [10]. They suggested that more extensive studies are required to train the
AI model for bone height and thickness measurements. Moufti et al. compared the
segmentation done by an AI model and a human investigator for a tooth-bounded
mandibular edentulous area and noted the acceptable accuracy of the AI model [11].
This is the first stage of implant planning, and automation of bone-level
assessment on a CBCT has further potential to reduce the overall time and cost
required for dental implant treatment. Similar results were also obtained by
Fontenele et al. for alveolar bone segmentation of the maxillary alveolar region
[12]. However, they noted that the manual segmentation had a slightly better
accuracy rate and the time required for the AI model was 116 times less than the
manual approach.
While the measurement of bone height and width for implant placement by AI has met
with limited success, research states that AI can serve as a valuable tool for the
detection of anatomical landmarks. Kwak et al. noted the successful detection of
the mandibular canal using a deep CNN model [13]. They stated that AI can serve as
a reliable tool for canal determination and play a significant role in implant
planning in the future. Similar results were also noted by Oliveira-Santos et al.
where the mandibular canal, along with its variation (anterior loop), was
determined by AI with high accuracy [14]. For the segmentation of the maxillary
sinus, the AI model used by Morgan et al. provided consistent automatic
segmentation, which could allow for the precise reproduction of 3D models for
diagnosis and treatment planning [15].
A case report was published by Mangano et al. where they combined AI and augmented
reality for guided implant surgery planning in a partially edentulous patient [16].
They believed that their novel protocol was efficient and time-saving for simple
cases of guided implant surgery. An interesting study was performed by Sakai et al.
where they used pre-op CBCT scans to predict implant drilling protocols [17]. Three
drilling protocols were analyzed - conventional drilling protocol with a tapping
drill, conventional drilling protocol without a tapping drill, and undersized
drilling protocol. A precision accuracy of 93.7% was noted, thereby suggesting that
AI can be used to predict the primary stability of implants based on the drilling
protocols pre-surgery. This could be of great help to young clinicians who are at
the start of their implantology careers.
Prosthetically driven implantology requires a precise 3D placement of the dental
implant. As of today, the use of AI can be an asset to treatment planning in
implantology by assisting clinicians in the decision-making process. Further
research is required to let the AI model run point on 3D planning of the future
dental implant.
Detection/Recognizing Implant Type/Brand
There are several brands of implants available currently all over the world. These
implants have different abutments and different prosthetic components. In case of
any complications with the implants or their super-structures, additional
prosthetic, surgical, or periodontal procedures are required. Additional
information, such as implant manufacturer, diameter, length, platform, and abutment
type, is required during these problem-solving appointments. This information is
easily accessible if the implant treatment was performed by the same clinician. If
the procedures have been performed at another clinic, and the treatment provider
cannot be contacted, it may be difficult or even impossible to get this
information. The use of AI for implant brand detection is a potential solution to
this increasingly complex problem.
In clinical practice currently, there are already two systems for implant
detection. The first system uses a website (www.whatimplantisthat.com), which
contains a database of radiographs of different implant brands wherein dentists are
required to check if their radiograph images match the website image [18]. The
second system developed by Michelinakis et al. uses a questionnaire about implant
characteristics, and it requires the dentists to match the answers with the
database to identify the implant [19]. However, both these systems require the
clinician to match the radiographic image to the database, thus increasing the
element of human error in the identification process. The advantage of AI is that
the computer identifies the implant instead of the dentist. The CNNs of the DL
family can identify images by forming an identification algorithm in which they can
detect the spatial hierarchies of features, such as edges, textures, and shapes
[20].
Literature on the accuracy and feasibility of the detection of different dental
implant systems (DIS) by AI is now emerging. A systematic review by Chaurasia et
al. concluded that DL should be used as a decision-aid tool for experienced
clinicians to increase the accuracy of the detection of DIS [21]. They believed
that, since DL algorithms are constantly evolving, it is not possible to classify
DIS solely based on these data, and clinical knowledge should be backed by AI to
make this decision. In a pilot study by Takahasi et al., 1,282 panoramic
radiographs with six implant systems from three manufacturers were used as a
dataset [22]. Specifically, 80% of the images were used as a training dataset, and
20% as the testing dataset. The mean average precision of their model was 0.71, and
the mean intersection over union was 0.72. A systematic review by Revilla-León et
al. noted the overall accuracy range of 93.8%-98% of the AI models in the different
reviewed studies [23]. Most of the reviewed studies extracted data from a 2D
radiograph, such as a periapical or panoramic radiograph, instead of CBCT. As of
now, CBCT has not been used for data extraction to train AI models. This is also
supported by a study by Correa et al. who have raised queries on the resolution and
sharpness of CBCT and suggested that it may be lower as compared to peripheral
radiographs [24]. Hence, whether CBCT can be used for the classification of DIS by
AI is debatable [24].
There have been multiple studies with a diverse number of DIS and variable datasets
to test the accuracy of AI in image detection. A multi-center study evaluating
156,965 panoramic and periapical radiographs by Park et al. noted high accuracy for
both 2D radiographs [25]. Similar results were obtained in several other studies
[20,26-28]. All these studies, while noting the high accuracy of the AI model in
the identification of DIS, have suggested expanding the dataset to incorporate more
images of implant brands to get more precise results. The results of all these
studies, however, cannot be co-related as they are all performed under different
conditions, with different CNN models, varied numbers of training and test images,
and different implant brands.
The major implant systems vary in different parts of the world. Creating an
accurate database for each implant is the need of the hour. The formation of a
regional-based DL model for accuracy verification is also very important. Adherence
to medical ethics while using big data, for building a global dental implant
classification system, will effectively contribute to dental care all over the
world.
Development of New Implant Designs
There have been a few studies that have applied an AI model for implant design
optimization using finite element analysis (FEA). FEA is a mathematical model that
determines the mechanical behavior of dental implants, especially stress
concentration at the implant-bone interface [29]. Li et al. developed an AI model
to measure the stress at the implant-bone interface by considering the implant
length, implant thread length, and thread pitch [30]. This model, in comparison
with the FEA model, noted a reduction of 36.6% stress at the interface. Roy et al.
proposed to modify the implant geometry (implant length, porosity, and diameter)
with a combination of ANNs and genetic algorithms to achieve the desired micro-
strain at the implant-bone junction [31]. Zaw et al. used a reduced-basis method to
train a neural network model to accurately measure the elastic modulus of the bone-
implant interface [32]. Further research is required to improve the applicability
of AI in the development of new implant designs with more in vitro, animal, and
clinical studies.
Risk Assessment
1
Preoperative Risk Prediction
Al models assess patient data to predict surgical risks
2
Intraoperative Risk Monitoring
Real-time Al analysis of surgical progress and patient vitals
3
Postoperative Outcome Prediction
Al forecasts recovery trajectories and potential
Prediction of Treatment Outcomes in Implantology
As dental implants continue to be the most preferred treatment modality for both
patients and clinicians, implant complications are also on the rise. Implant
complications lead to increasing costs and additional procedures for both the
patient and the clinician. It is difficult to predict implant loss or its
complications since there are many risk factors involved, such as patient
characteristics, type and quality of alveolar bone, implant type, and surgical
plan. Implant failure or loss is generally predicted by clinicians based on their
clinical knowledge and experience. applications of AI in
implant prosthodontics have not yet been published extensively, a retrospective
clinical study by Lerner et al. demonstrated the use of AI in the restoration of 90
patients with 106 implant-supported monolithic zirconia crowns [42]. They utilized
the AI feature of computer-aided design (CAD) software to fabricate the final
restoration that confirms the gingival contour even after tissue maturation
following a temporization phase. Even though the AI used in this method was a
"weak" AI, they believed that this method would allow dental technicians to save
time and reduce the costs and errors of the final prosthetic process.
A study by Hwang et al. developed a DL model for automatic classification of
surgical plans for sinus augmentation procedures in the maxillary posterior region
[43]. They utilized the anatomical landmarks noted on a CBCT scan to train their AI
model. The classification put forth by their model was then compared to the ABC
classification proposed by Wang et al. [44]. Accurate detection of the anatomical
landmarks and accurate classification of the sinus floor augmentation procedures
make it a handy tool while planning the rehabilitation of patients with missing
maxillary posterior teeth.
Prediction of Treatment Outcomes in Implantology
As dental implants continue to be the most preferred treatment modality for both
patients and clinicians, implant complications are also on the rise. Implant
complications lead to increasing costs and additional procedures for both the
patient and the clinician. It is difficult to predict implant loss or its
complications since there are many risk factors involved, such as patient
characteristics, type and quality of alveolar bone, implant type, and surgical
plan. Implant failure or loss is generally predicted by clinicians based on their
clinical knowledge and experience. Prediction of treatment outcomes in implantology
is the need of the hour, and AI has the potential to be a major contributor to this
field.
The literature in this field is currently very limited with only singular articles
with very limited follow-up. No systematic reviews and meta-analyses have yet been
published on the prediction of treatment outcomes in implantology. Lyakhov et al.
proposed a neural network model for predicting survival rates of single dental
implants by analyzing the statistical factors of the patients [33]. They formulated
their database based on the case histories and the clinical condition of the
patient. Their model noted an accuracy rate of 94.48% for single implant survival.
They, however, concluded that this model cannot be independently used for decision-
making but can surely assist the clinician as a diagnostic tool in implantology.
Oh et al. noted that osteointegration of dental implants can be predicted to some
extent by AI with plain radiographs and can complement the existing
osseointegration determination methods [34]. Seven different DL models compared two
groups of implants - one which was immediately placed and the others were
radiographed after successful osteointegration. Cha et al. used an ML model to
measure peri-implant bone loss on periapical radiographs [35]. While they believed
that the model could assist clinicians in diagnosing and classifying peri-
implantitis, in the current study, they did not find any statistically significant
difference between the bone loss levels calculated by the dentists and the AI
model.
Literature has reported articles that have predicted the risk of implant loss using
neural networks. Huang et al. suggested that their predictive AI model can suggest
the implant fate within five years, which will help dentists identify high-risk
patients and accordingly modify their treatment plans [36]. Three models - a
clinical model, a DL-based radiographic model, and an integrated model (by
combining the clinical and radiological DL model) - were developed to predict the
five-year implant loss risk. The integrated model had the best prediction rate as
far as five-year implant loss risk was concerned. Another DL model predicting
implant loss was developed by Zhang et al. based on periapical and panoramic films
[37]. Per-implant alveolar bone loss levels were analyzed, and a prediction
accuracy of 87% was obtained. The results of this study were also in accordance
with the results of Huang et al. [36], where the model can accurately predict the
risk of implant loss, thus aiding clinicians in the decision-making process.
Further clinical evidence with long-term studies with a greater number of implants
and brands is required before fully integrating AI into clinical practice.
Robotic Implant Surgery
The integration of robotics and AI in dentistry is called "dentronics" [38].
Precise surgical placement of a dental implant is essential to prevent any
complications in both the surgical and prosthetic phases. In 2017, the Food and
Drug Administration (FDA) approved the robotic surgical assistant for the placement
of dental implants. Based on CBCT scans, the implant position is planned by the
dentist, and the robotic arm performs the surgery with the dentist observing the
procedure in real time - which gives the dentist the flexibility to change any
angulations intraoperatively [38]. Such a case was performed in China in 2017 where
two implants were placed in a patient by a robot without any intervention by the
dentist. Several clinical reports have been published in the literature where
successful implant placements have been done by robots [39-41].
A probable reason for robotics being a low-demand field in dentistry is the lack of
expert knowledge. Additionally, research in this field requires collaboration
between dentists and engineers. The use of AI can help the emergence of robotics in
implant dentistry. AI can analyze large patient datasets to help in diagnosis and
treatment planning, thereby optimizing the implant process.
• Implant success hinges on perfect placement as well as excellent
patient understanding of the procedure and follow-up care.
• AI systems that can assist in identifying implant sites are in
development, while those that can measure and label bone height already exist.
• Using an AI system to help educate patients about oral pathology and
treatment plans can help increase the chances of lasting treatment success.
Improving dental implant placement accuracy
Pinpointing the exact location of the mandibular canal has typically relied on x-
ray and CT modeling, a tedious process due to the complexity of the human lower
jaw.
New models using AI technology are emerging that can help simplify this process.
One particular research-based model being developed by researchers at the Finnish
Center for Artificial Intelligence (FCAI), Tampere University Hospital, Planmeca,
and the Alan Turing Institute, is designed to accurately and automatically identify
the exact location of the mandibular canal for dental implant operations.
Eventually, the model is intended to reduce the risk of paresthesia during implant
surgery by providing support for radiologists. As one researcher put it, “The aim
of this research work is not to replace radiologists, but to make their job faster
and more efficient so that they will have time to focus on the most complex cases.”
Improving dental implant treatment planning
Patient quality of life improves with dental implant placement, but only if the
procedure has lasting success. Factors that can endanger the success of implant
surgery include:
• Improper follow-up care by the patient
• Infection and inflammation (peri-implantitis)
• Bone loss
The existence of any of these factors can be identified by radiographic bone
changes, and AI is among the advancements that can flag early changes that might
otherwise be hard to judge. Identifying pre-procedure bone loss is particularly
critical to treatment planning.
While CBCT scans are superior for measuring bone structures, 2D periapical
radiographs can also be used. AI systems can augment these solutions and provide an
advantage in both treatment planning and patient education.
Encouraging patient preventive care
Convincing patients that the health of their implant is not limited to what they
can see with the naked eye can help them maintain good oral hygiene practices at
home. By labeling radiographs with easy-to-understand information about bone
height, subgingival calculus, and more, AI-based technology supports the dialogue
around implant follow-up care, as well as everyday conversations about:
• Routine preventive care
• Periodontal health
• Effective oral hygiene
• Systemic health and wellness