Artificial Intelligence and Its Impact in Endodontics

By Dr. Tung Bui, D.D.S., FICD

Artificial Intelligence (AI) has recently garnered exponential interest and adoption due to the success of OpenAI’s generative AI service, ChatGPT. Emerging as a powerful tool, AI carries transformative potential across various fields, including endodontics. By leveraging AI, endodontists can enhance clinical practices, optimize business operations, and elevate patient experiences. This article delves into the history and basics of AI, explores its inner workings and machine learning, and examines its specific applications in the field of endodontics.

The rich history of AI dates back to the 1930s when Alan Turing, often considered the father of AI, questioned, “can machines think?”. Predicting that someday differentiating intelligence between humans and machines might become challenging, Turing developed the Imitation Game, better known as the Turing Test. Today, we often validate our human identity through CAPTCHA tests, which stands for “Completely Automated Public Turing test to tell Computers and Humans Apart”. The discipline of AI was officially formed in 1956 during the 8 week long Dartmouth Conference by computer scientists, cognitive psychologists, and mathematicians, but the lack of computational power led to an AI Winter lasting several decades. Fast forward to the 2010s, the groundbreaking multilayered neural network developed by University of Toronto’s Geoffrey Hinton and his graduate student, Alex Krizhevsky, marked a turning point in the AI revolution. AI has witnessed significant advancements since, including expert systems, natural language processing, neural networks, and deep learning.

AI terms that endodontists should be familiar with include:

• Artificial Intelligence: A branch of computer science that creates intelligent machines mimicking human cognitive abilities like learning, problem-solving, and decision-making.
• Machine Learning: A subset of AI that enables machines to automatically learn from data without being explicitly programmed.
• Supervised Learning: A subcategory of machine learning where algorithms are trained using labeled datasets, analogous to a teacher instructing a student.
• Unsupervised Learning: A subcategory of machine learning where algorithms analyze unlabeled datasets to find patterns, similar to self-directed student learning.
• Reinforcement Learning: A subcategory of machine learning where algorithms interact with their environment and learn through a reward and penalty system, analogous to a toddler learning to walk.
• Deep Learning: A type of machine learning that simulates the complex learning and decision-making processes of the human brain using artificial neural networks.
• Computer Vision: A form of AI that enables algorithms to interpret and recognize images similarly to how humans do.
• Natural Language Processing: A type of AI that allows algorithms to manipulate, understand, and interpret human language.
• Generative AI: A type of AI where algorithms can generate text, images, videos, and other types of media based on human prompts.
• General AI: A hypothetical level of AI that can solve unfamiliar problems in a manner similar to human intelligence.

AI mimics natural intelligence. It processes large volumes of data, identifies patterns, and extracts insights to guide decision-making processes. AI’s architectural design is reminiscent of the structure of human neurons, with inputs entering the artificial neuron and undergoing mathematical computations to produce an output. These artificial neurons connect to form a neural network. As AI systems encounter new information, machine learning algorithms enable them to adapt and improve their efficiency and accuracy over time.

Building our own AI machine involves several key steps:

1. Data Collection: Gathering relevant datasets, which may include clinical records, radiographic images, and patient outcomes.
2. Data Preprocessing: Cleaning and preparing the data to ensure its quality and compatibility with the machine learning algorithms.
3. Feature Extraction: Identifying relevant features from the data that can contribute to accurate predictions or classifications.
4. Algorithm Selection: Choosing appropriate machine learning algorithms, such as decision trees, support vector machines, or deep neural networks, based on the specific task at hand.
5. Model Training: Training the selected algorithm using the prepared data and adjusting its parameters to optimize performance.
6. Model Evaluation: Assessing the model’s performance using validation datasets and metrics such as accuracy, sensitivity, and specificity.
7. Deployment and Iteration: Implementing the model in practice, monitoring its performance, and making iterative improvements as new data becomes available.

AI’s usage in endodontics is already widespread, and the following are just a few examples of its applications:

1. Clinical Applications:
   • Computer Vision in CBCT Scans: AI-powered computer vision techniques can assist in analyzing CBCT scans, aiding in identifying anatomical structures, cracks, pathologies, and assisting in treatment planning.
     • – Metal Artifact Reduction Filters: AI algorithms can reduce image distortion caused by metallic restorations or implants, enabling clearer and more accurate interpretation of CBCT scans.
     • – Diagnosis Support: AI models can analyze patient data, including radiographic images, clinical history, and symptoms, to assist endodontists in making accurate diagnoses and treatment recommendations.

2. Business Applications:
   • Automation of Practice Management: AI systems can automate various administrative tasks, such as tracking referrals, appointment scheduling, billing, inventory management, streamlining practice operations, and reducing manual workloads.

 

3. Patient Experience:
   • Chatbots on Practice Websites: AI-driven chatbots can provide instant responses to patient queries, offer appointment scheduling assistance, and deliver relevant educational resources, enhancing patient engagement and satisfaction.

Besides the above applications, AI promises future developments in endodontics. Research is underway to leverage AI for automated analysis of histopathological samples, crack detection, predicting treatment outcomes based on patient characteristics, and personalized treatment planning.

While AI heralds an exciting new era in endodontics, its implementation comes with potential challenges. Privacy and security of patient data, ethical use of AI algorithms, and the necessity for continued human oversight must be carefully addressed to ensure the responsible and effective integration of AI technologies into endodontic practices. At the organized dentistry leadership level, discussions are underway to navigate our profession through the AI revolution.

In conclusion, artificial intelligence is transforming the field of endodontics, offering new opportunities to enhance clinical decision-making, streamline practice management, and improve the overall patient experience. As AI continues to advance, endodontists can leverage its capabilities to improve diagnostic accuracy, optimize treatment planning, and ultimately provide better outcomes for their patients.

To delve deeper into endodontic-related AI, consider listening to the latest AAE Endo Voices podcast, where host Dr. Marcus Johnson interviews Dr. Asma Khan and Dr. Frank Setzer on the topic of AI and its use cases for endodontics.

Dr. Tung Bui is a Board-certified Endodontist practicing in Tucson, Ariz. He also serves as a clinical instructor and lecturer at the NYU Langone and Spartanburg Regional Healthcare System AEGD programs. When not saving teeth, Dr. Bui is roasting exquisitely rare coffees and finding excuses to be outdoors. A futurist investor, Dr. Bui invests his time into exploring emerging and disruptive technologies. He currently chairs the AAE Connection Committee. Disclosure: The author has no financial interests, and the opinions expressed are solely his and not those of the AAE.  ChatGPT-4 was used as an editing tool. You can contact Tung Bui at apexologist@gmail.com.