The use of artificial intelligence in orthodontics

Author:

Kuzyk I.M.1ORCID,Kotelban A.V.1ORCID

Affiliation:

1. Bukovinian State Medical University, Chernivtsi, Ukraine

Abstract

The application of Artificial Intelligence (AI) in orthodontics is very diverse and ranges from the identification of anatomical and pathological structures of the human dentition to support complex decision-making in orthodontic treatment planning. Its application has grown significantly in recent years, as reflected by the exponential increase in the number of scientific publications on the integration of artificial intelligence into everyday clinical practice. In many cases, AI can be seen as a valuable tool whose algorithms help dentists and clinicians analyze data from multiple sources of information. The purpose of this paper was to analyze current views on the use of artificial intelligence techniques and models in orthodontics based on a literature review. The scientific publications of various scientometric databases (PubMed, Scopus, Google Scolar, Web of Science, etc.) over the past 5 years were processed. Artificial intelligence is one of the most promising tools due to its high accuracy and efficiency. Given the current scientific dynamics in the field of AI, it can be assumed that AI will become an integral part of diagnostics and treatment planning in the near future. Practicing dentists will be able to use it as an additional tool to reduce their workload. However, this requires close cooperation of commercial AI products with the scientific community, further research, including randomized clinical trials, to test and integrate this concept in dental practice. Modern artificial intelligence is excellent at utilizing structured knowledge and gaining insights from huge amounts of data. However, it is not able to create associations like the human brain and is only partially capable of making complex decisions in a clinical situation. In turn, the efficiency of AI is achieved only when unbiased training data and a properly designed and trained algorithm are used. Keywords: dentistry, diagnostic, machine learning, cephalometry.

Publisher

Kharkiv National Medical University

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