Promises and perils of artificial intelligence in dentistry - PubMed Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2021 Jun;66(2):124-135.
doi: 10.1111/adj.12812. Epub 2021 Jan 17.

Promises and perils of artificial intelligence in dentistry

Affiliations
Free article
Review

Promises and perils of artificial intelligence in dentistry

F Pethani. Aust Dent J. 2021 Jun.
Free article

Abstract

Artificial intelligence (AI) is a subdiscipline of computer science that has made substantial progress in medicine and there is a growing body of AI research in dentistry. Dentists should have an understanding of the foundational concepts and the ability to critically evaluate dental research in AI. Machine learning (ML) is a subfield of AI that most dental AI research is dedicated to. The most prolific area of ML research is automated interpretation of dental imaging. Other areas include providing treatment recommendations, predicting future disease and treatment outcomes. The research impact is limited by small datasets that do not harness the positive correlation between very large datasets and ML performance. There is also a need to standardize research methodologies and utilize performance metrics that are appropriate for the clinical context. In addition to research challenges, this article discusses the ethical, legal and logistical considerations associated with implementation in clinical practice. This includes explainable AI, model bias, data privacy and security. The future implications of AI in dentistry involve a promise for a novel form of practicing dentistry however, the effect of AI on patient outcomes is yet to be determined.

Keywords: artificial intelligence; dentistry; imaging; machine learning; neural networks.

PubMed Disclaimer

Similar articles

Cited by

References

    1. Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med 2018;1:39.
    1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019;25:44-56.
    1. Russell SJ. Artificial intelligence : a modern approach, 3rd edn. Harlow: Pearson Education Limited, 2016.
    1. Marsland S. Machine learning: an algorithmic perspective, 2nd edn. Boca Raton: CRC Press, 2015.
    1. Mitchell TM. Machine learning. New York: McGraw-Hill, 1997.

LinkOut - more resources