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AI in implant dentistry: clinical implementation and considerations for use

Continuing Education (CE)

The continuing education article below is available to Implantologists and general dental practitioners who perform implants.

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Educational aims and objectives

This self-instructional course for dentists aims to identify how artificial intelligence (AI) works, as well as its advantages and disadvantages in the dental practice.

Expected outcomes

Implant Practice US subscribers can answer the CE questions by taking the quiz online to earn 2 hours of CE from reading this article. Correctly answering the questions will demonstrate the reader can:

  • Define AI and the evolving types of AI.
  • Identify the basics of how the data is used in a clinical way by interacting with AI-enabled systems and machines.
  • Recognize how AI systems are being used to improve aspects of the implant process.
  • Realize some of the issues that need to be addressed to ensure patient safety, clinical success, and to safeguard the practice from legal complications.

Drs. Aaron Glick, Nicholas Tipton, Nisha Manila, and Jennifer Chang discuss AI in the dental practice, and what impact it can have on the implant process.

Drs. Aaron Glick, Nicholas Tipton, Nisha Manila, and Jennifer Chang discuss the pros and cons of AI in the dental practice

Introduction

Although artificial intelligence (AI) has been integrated in daily life, most are not aware that it has become an integral part of many aspects of modern life over the past few decades given the increased computing power, digitization of data, advances in algorithm, and improvements in accuracy. In fact, when a group of Americans were surveyed, over half were not able to identify that AI was the power behind wearable fitness trackers, automated chatbots, product recommendations, security cameras that identify unrecognized people, music playlist recommendations, and email services identifying spam messages.1 In dentistry, some of these same AI systems, such as chatbots, have been integrated for general office support and software automations. Other AI systems that are more dental-specific have also been used for approximately a decade. These include systems that automatically identify dental landmarks in CBCT,2 maxillary/mandibular segmentation from radiographic images,3 and digital reconstruction of tooth surfaces.4

Some examples of more current AI systems in dentistry are decision-support systems to aid clinicians in the diagnosis of caries and periodontal conditions, AI-enabled robotic surgeries, and creation of AI-generated dental restorations/appliances.5 Since AI is a tool, it can be used in multiple applications within the dental field, and as the algorithms advance in accessibility and accuracy, the amount of products with embedded AI likely will increase (Table 1). Therefore, it is important as a dental practitioner to understand the tools used, particularly if they can have a direct or indirect effect on patients.

Table 1: Multifaceted uses of AI in dentistry Dental specialty Use case Endodontics • Identify root morphology, minor apical foramen • Diagnosis of apical lesion, root fracture Implant dentistry • Risk of peri-implant diseases • Surgical procedures Oral surgery • Identification of anatomic features • Screening and classification of oral cancer Orthodontics and pediatric dentistry • Cephalometric analysis • Treatment needs and outcome evaluation • Vertebral growth assessment Pathology • Analyze histologic slides and cytologic specimens Periodontics • Clinical and radiographic assessment • Immunologic and microbiologic analysis Prosthodontics and restorative dentistry • Caries detection • Shade selection, tooth preparation, restoration design Radiology • Radiographic mounting • Caries detection, oral cancer diagnosis Non-clinical • Patient education and engagement • Dental research • Practice management (i.e., HR and insurance automations, financial projections, scheduling chatbots)

How AI works

AI is a broad term that generally refers to the ability for computers to perform tasks that typically require human intelligence (Figure 1); for example, viewing a dental X-ray and identifying interproximal caries. Machine learning (ML) refers to the mathematical approach to accomplish this perceived human intelligence. These algorithms are programmed to take information and “learn” from these datasets. Since there are alternative methods for artificial intelligence, machine learning is considered a subset of the approaches to achieve artificial intelligence. Multiple types of algorithms can be programmed by the user. In one approach, a convolutional neural network (CNN) can feed a large dataset to identify the features that are most salient in a classification task. For instance, with multiple pictures of apples or cats, the neural network would be a mathematical model used to make the binary decision on if the currently presented image should be classified as an apple or cat. The CNN approach is typically selected for image operations since this algorithm is optimized to process images based on the extraction of spatial features.

Depending on the task to be performed, there are multiple types of models that are specific to optimizing for the type of data or output intended. For instance, chatbots require a text-based approach with grammatical rules built into sequences of words. Therefore, language-specific models would be used for text-based tasks, typically called natural language processing (NLP).

Figure 1: Visualization of the distinction between terminology for artificial intelligence (AI), machine learning (ML), and Convolutional Neural Networks (CNN)

Further into the black box

While most dentists today don’t consider building custom AI products, there are products that will democratize the use of AI through no-coding technologies.6 Whether coding the algorithms or not, understanding the basics of how the data is used to derive a clinical decision/outcome is important since dentists are increasingly interacting with AI-enabled systems and machines.

When software manufacturers train an AI model, they require a large amount of data to process. In general, the full dataset is split into 3 distinct groups: 1) training set, 2) validation set, and 3) testing set (Figure 1). In a supervised model, a team of clinicians will label the full dataset. For instance, if the classification task is to automatically identify caries in bitewing radiographs, the clinician would identify areas of the radiographs with caries. These labeled radiographs serve as the “ground truth” for the algorithm. The bulk of the data is used in the training set to optimize the weights of the neural network identifying salient features of the categorized dataset. The model is then further optimized by using a validation dataset, where additional processes can improve the accuracy. The final step is to understand how accurate the fully trained AI model can perform on the similar dataset. Therefore, the testing dataset is then categorized by the trained model to provide an accuracy typically reported as a percentage of accurately categorized responses and Receiver Operating Characteristics (ROC) curve/area under the curve (AUC). The ROC curve and AUC offer additional details similar to the true positive rate and false positive rate. Ultimately, the large dataset is trained, and the same isolated dataset is used to assess the accuracy in a non-clinical setting.

When building these algorithms, there are some additional key considerations as to how the algorithm uses data. In an oversimplification, an algorithm can be delineated as 1) supervised/unsupervised or 2) static/continuous learning. Supervised learning is based on a clinician providing the algorithm with a ground truth for labelled data, similar to the previous example. Unsupervised learning is where the model learns without explicitly provided information to classify and can be accomplished through patterns within the data. Static learning algorithms are the traditional type of AI, however, some current algorithms are designed to update and improve themselves based on additional data acquired during use.

Figure 2: Use of data with a supervised AI model

AI in implant dentistry

AI is changing our daily lives and approaches to dentistry in general. In implant dentistry, AI has spurred innovations in automations, assistive technology, and predictive outcome recognition. There are multiple examples of AI systems being used to improve aspects of the implant process.

The first example is identification of the implant type in radiographs. The accuracy of these models was generally high and ranged from 91% to 98%.7 Another example of AI in implant dentistry is the prediction of osteointegration success, which depending on the study and model type used, was between 60% to 89%.7 Since a host of variables potentially dictates the success/failure of a placed implant, AI has the potential to identify surgical and host factors personalized to the specific case to predict later failure cases. Each implementation of AI uses different variables such as patient age, bone thickness, smoking, radiographic inputs, and implant placement location.

Optimization of implant design to reduce implant-bone interface stress is another example of using AI in dentistry. In these examples, an AI-generated implant body was tested based on simulated parameters.7 To generate the implants, some of the input parameters were implant length, diameter, porosity, and pitch of thread.

Additional examples are software features for implant planning such as generative tooth libraries and anatomical segmentations.5 Generally, automated, or near-automated steps during the process of implant planning in 3D digital space or fabrication of surgical stents are used to aid in the efficiency of the clinicians’ implant workflow.

Lastly, AI can be used in the research of implant dentistry. In large datasets, there is potential to identify unknown patterns that could have clinical significance. In one research project conducted by the authors (JC, NT, AG), a Shapley Additive exPlanations (SHAP) analysis was performed, that allows explainable machine learning to measure the impact of each variable on the ultimate output of the model. In this case, a total of 205 patient records at the University of Texas Health Science Center at Houston School of Dentistry were used to determine the development of peri-implantitis, a risk factor for long-term implant health. Despite manually checking for data validation and accuracy for all patient records, the model found that race, osteoporosis, and recreational drugs were the most salient features of the model as a risk factor for peri-implantitis over other categories (Figure 3).

Figure 3: SHAP analysis on patient records showing colors for importance of each variable on the outcome of peri-implantitis. Red color means that it highly corresponded, and blue means that it did not correspond to peri implantitis. The SHAP value relates to a positive or negative relationship to the outcome of peri-implantitis

A light in the black box

AI presents an exciting tool to simulate human intelligence to help dentists achieve a higher quality of life, better decision-making capability, and fewer postoperative complications.8 However, despite these advances, AI in dentistry is in its infancy. On the horizon are more autonomous systems that do not require human input at the clinical stage within the AI algorithm. While a good understanding of the basics of AI is useful, the actual processes of most algorithms and datasets are not transparent to clinicians. Accuracy, trust, data stewardship, and equity/sustainability are concerns that clinicians and AI software manufacturers need to address to ensure patient safety, clinical success, and safeguarding from legal complications.

In implant dentistry, AI has spurred innovations in automations, assistive technology, and predictive outcome recognition.

Accuracy

The accuracy of an AI system is based on a predefined gold standard. Most clinical decision support tools are supervised algorithms and labeled by clinical dentists without histologic confirmation. Since the amount of data that is required to train the models is so large, it is impractical to build highly accurate datasets that are verified with high-quality standards. In fact, the sensitivity for dentists to detect interproximal caries in bitewings in dentin is low (.36),9 and provider agreement also is low (.4 kappa).10 Similar findings have been seen in other fields such as in periodontal conditions yet are task specific.11 While the quality of the dataset will likely not be transparent, the process for labeling the data as an important factor in the accuracy of the system could be stated by the manufacturer. Another issue that can affect AI is the imbalance of certain conditions like rare pathology that limit the number of instances for the algorithm to learn.

What is the clinical accuracy of these AI models? Is the trained dataset similar to your patient population or data in your EHR? Most report accuracies on the original dataset that was used, yet the true accuracies with different datasets or after clinicians have interacted with the AI system remain limited, and independent testing remains even more limited. In fact, some studies have shown that when AI presents incorrect information, clinicians are more likely to be misled by this recommendation which could result in poor patient outcomes.11,12 Should incorrect guidance be provided by the AI system and patient harm occurs, the current legal climate would hold the clinician liable.13 Additionally, when the AI system is accurate, there are concerns of other biases and trust.

Trust

A human-computer interaction exists between the dentist and AI system that can affect the perception of clinical information and ultimately treatment performance and decision. Trust is a well-studied concept that is affected by many factors with transparency, reliability, and tangibility as some of the commonly cited values that apply most to human-computer interaction.14 Transparency is inherent in trust, and AI systems can be built to be explainable. While most traditional forms of AI suffer from the “the black box problem” (the inability for people to see how learning systems such as AI make their decisions), some offer detection strategies through computer vision,15 and others provide embeddings through natural language processing.16 Reliability offers a perception that the algorithm is making similar decisions in seemingly similar circumstances. For continuously learning algorithms, there is a potential risk that adaptive learning could introduce new errors and reduce reliable performance.17,18 Tangibility refers to the physical or virtual presence of the AI system and when it appears more real or anthropomorphic, which improves trust.19

Data stewardship

Machine learning sits at the intersection of big data and software algorithms. Given the need for large datasets to train models, the data stewardship in regulated industries, especially dentistry, is important. Dental offices handle private data on patients within the electronic health records system, such as financial, demographic, disease state information, and other sensitive clinical information. With the digitization of records and equipment, the dental industry is a prime target for hackers and also lacks adequate knowledge about the prevention of these crimes.20 In fact, Henry Schein, a large distribution and software company with more than 23,000 employees and 1 million customers globally, halted operations due to a cyber-security incident.21 Large, consolidated datasets with patient data are vulnerable to attacks. Additionally, generative AI that ingests data from multiple sources feeding back into the algorithm can pose a risk if patient data is used. For instance, divulging patient data on a generative AI platform to write patient notes more efficiently could put the dentist at risk of breaching HIPAA with patient data being used to continuously train the model. For example, Sarah Silverman is suing openAI and Meta over violations of her copyrighted book that surfaced when these platforms regurgitated substantial elements of her copyrighted materials.22

Equitability/Sustainability

The equitable treatment of patients is an ethical value upheld by the American Dental Association where all treatment decisions should be determined on the same basis as all other patients.23 As AI systems are built with human inputs, biases can be encoded into the model.24 In one example of this, Optum, a health service company, created an AI program to spot high-risk patients that should receive medical follow-up care. However, the follow-up care disproportionately identified white patients with 82% white and 18% black patients identified compared to an independent analysis finding that 53% white and 46% black patients should have been identified based on medical need. Before this error could be detected, the program had already been applied to 100 million patients.25 Additionally, the hardware that powers AI requires high energy and a large local ecological footprint.26 In fact, GPT3 took 1.287 gigawatt hours of electricity and 700,000 liters of clean freshwater.27

Implementation of AI systems

As manufacturers continue to release new and update current AI embedded technologies, dentists should be aware of how these tools operate and can potentially change patient care. Many dentists are likely to implement embedded AI technologies without fully realizing it since AI is commonly used as a tool to accomplish a task or optimize tasks, and most backend systems are not transparent to the user.

In order to implement new technologies with AI into a dental office, technology acceptance and continual quality assurance should be a consideration. The primary factors for technology acceptance are perceived usefulness and perceived ease of use.28 Therefore, removing barriers to trust within the human-computer interaction can improve usage. Training with technology in a safe environment can build confidence and improve ease of use. Additionally, having a plan for continuous quality assurance with the use of AI can aid in patient safety given that the systems can change with updates or continuously learning models. AI behavior should be periodically monitored, and a set of well-defined metrics are needed to guide regularly scheduled testing.29

The U.S. government also functions to protect patients and dental offices’ implementation of AI through the FDA clearance process of software as a medical device. FDA has become increasingly more stringent on this type of software that predicts and identifies medical/dental conditions.30 As dentists expand their appetite for additional functionality of these software systems, there needs to be a balance between the regulatory burden and the ability to innovate.

Exciting future of AI in dentistry

With the digitization of dental records and radiographs, dentistry is an ideal profession to harness AI. Current applications of AI are already being implemented to change the workflow of dental offices and patient care. Despite risks of inaccuracies, human-computer mistrust, data security concerns, and inequities in applications, it is easy to imagine overcoming these obstacles as the perceived future benefits outweigh the risks.31 Healthcare in general is expected to see an increase in venture investments, as the value of AI in health is expected to grow from $4.9B in 2020 to $45.2B in 2026. Dentistry, however, is lacking in the amount and accuracy of data.32 To accelerate the advancement of AI, manufacturers need access to large, structured datasets that contain valid and reliable data. In fact, each of our practices holds valuable data that has the potential to improve the field.

The use of AI in the dental practice is growing. Read about an award won by a researcher who showed that the AI technology compares favorably with clinical providers in reading X-rays. https://implantpracticeus.com/industry-news/artificial-intelligence-technology-powers-award-winning-clinical-research-in-dentistry/

Author Info

Aaron Glick, DDS, works in healthcare innovation and is on faculty at the University of Texas Health Science Center at Houston School of Dentistry and Sam Houston State University College of Osteopathic Medicine. He is a Diplomate of the ABDSM, faculty member of the AADSM Mastery Program, and holds fellowship credentials with Academy of General Dentistry and International Congress of Oral Implantology. He enjoys all technologies and has a background in software programming for neuroscience research at Brandeis University and Harvard University using 3D modeling, AI, and fMRI technologies. Dr. Glick has been published in medical and dental journals and lectures nationally on dental sleep medicine and digital/software technologies.

 

Nicholas Tipton, DDS, is a current periodontics resident at Texas A&M School of Dentistry in Dallas. He previously completed his DDS training at UTHealth School of Dentistry at Houston in 2023 and undergraduate degree at Texas A&M in College Station. Nicholas has many research and clinical interests including implantology, hard and soft tissue augmentation surgery, and implementation of AI within periodontics research. After residency, Nicholas plans to work within private practice and teach part-time.

 

Nisha Manila BDS, PhD, is an Associate Professor and Director of Oral and Maxillofacial Radiology at California Northstate University College of Dental Medicine. With a dental degree from India, and a Ph.D. in Oral Radiation Oncology from Tokyo Medical and Dental University, Japan, she completed Oral and Maxillofacial Radiology training at Texas A&M University. Dr. Manila is a fellow of the American College of Dentists, and her research interests include applications of AI in dentistry, CBCT imaging, and advanced diagnostic imaging modalities. Dr. Manila is an active member of several professional organizations.

 

Jennifer Chang DDS, MSD, is an assistant professor and program director in the Department of Periodontics at The University of Texas School of Dentistry at Houston. She completed her Doctor of Dental Surgery degree and 2 years of general practice residency in Taiwan. She obtained her Master of Science in Dentistry degree in periodontics from Indiana University School of Dentistry. Dr. Chang is a board-certified Diplomate in Periodontology and Dental Implant Surgery. She maintains a part-time practice at the school’s faculty group practice and in the greater Houston area. Her clinical and research interests focus on improving treatment options for periodontal/peri-implant disease.

References

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