Dental AI

Dental artificial intelligence (Dental AI) refers to the application of artificial intelligence (AI) and machine-learning methods to oral healthcare data. These systems can be used to find patterns or make predictions that can aid in diagnosis, treatment, patient communication, or practice management.[1][2][3][4]

History and development

Research into AI for dentistry dates to the 1990s and 2000s, alongside early CAD/CAM and image-analysis work in dental radiology.[4] Recent developments in deep learning, especially those involving computer vision, such as convolutional neural networks, trained on large image datasets, led to a rapid improvement in performance, as well as a move from prototype technology to productization suitable for use in dental chairs.[4][2][5] Dental schools and continuing education programs started incorporating AI content in the 2020s.[6][7]

Definition and core technologies

The dental AI software accomplishes this task by using various dental images and patient data. Dental images and data used by the dental AI software include bitewing and periapical X-rays, complete mouth X-rays, detailed 3D images, intraoral images, and the patient’s medical history. The dental AI software utilizes several core technologies in accomplishing its task of assisting the dentist.[8]

First, the dental AI software utilizes machine learning and deep learning using programs that can learn from examples. Such programs are referred to as convolutional neural network (CNN) and can detect cavities and identify bone changes related to gum disease.[4][2][6]

The dental AI software utilizes computer vision, which enables the AI software to identify and quantify important features in images and data, whether they are 2D images or 3D images.[2] Natural language processing (NLP) is used for the AI software to understand written text and can automatically generate dental notes and communicate with the patient.[9][10][11] Furthermore, the dental AI software utilizes predictive analytics to identify patients that are more prone to dental complications and can suggest the best intervals for checkups or future dental procedures.[4][2]

Applications in dentistry

Reported clinical and operational applications include diagnostic assistance for caries and periodontal disease, treatment planning assistance, patient education overlays, quality assurance, curriculum assistance for dental education, and claims documentation.[4][6][7] Systematic reviews continue to find image-based applications such as caries detection with some variability in study design and a need for prospective validation.[2][6][7][12]

Academic research and clinical validation

Several peer-reviewed studies have measured the effectiveness of AI for applications such as interproximal caries detection and periodontal bone level assessment, showing improvements over unaided readings with a focus on bias within the dataset.[2] The Dental AI Council found variability among clinicians for diagnosis and treatment planning, suggesting the use of a standard tool as an assist.[13]

Regulations

In the U.S., AI-enabled dental imaging software is generally reviewed via the FDA’s 510(k) pathway.[14] The FDA maintains a public AI-Enabled Medical Devices List, which includes numerous medical-imaging AI tools (including dental).[15] Specific dental clearances include Overjet (K210187), VideaHealth (K232384), and Pearl entries such as “Second Opinion 3D” (K243989).[14][16][15]

References

  1. ^ "New Calibration Tool for Oral Radiology | UCLA School of Dentistry". dentistry.ucla.edu. 2025-05-11. Retrieved 2026-02-05.
  2. ^ a b c d e f g "Artificial intelligence in dentistry - A review". Frontiers in Dental Medicine. 4. 2023-02-20. doi:10.3389/fdmed.2023.1085251. ISSN 2673-4915.
  3. ^ Gupta, Ekta; Maysan, Siddeeq; Murella, Susmitha; Saju, Anitta Rachel; Grover, Silvi; Vasudeva, Agrima (2025). "Recent dental practices using Artificial Intelligence (AI): A survey". Bioinformation. 21 (3): 514–521. doi:10.6026/973206300210514. ISSN 0973-2063. PMC 12208262. PMID 40599924.
  4. ^ a b c d e f Khanagar, Sanjeev B.; Al-ehaideb, Ali; Maganur, Prabhadevi C.; Vishwanathaiah, Satish; Patil, Shankargouda; Baeshen, Hosam A.; Sarode, Sachin C.; Bhandi, Shilpa (2021-01-01). "Developments, application, and performance of artificial intelligence in dentistry – A systematic review". Journal of Dental Sciences. 16 (1): 508–522. doi:10.1016/j.jds.2020.06.019. ISSN 1991-7902. PMC 7770297. PMID 33384840.
  5. ^ Martin, Scott (2022-04-21). "Tooth Tech: AI Takes Bite Out of Dental Slide Misses by Assisting Doctors". NVIDIA Blog. Retrieved 2026-02-05.
  6. ^ a b c d "Pitt Dental Medicine Awarded Innovation Grant in Education Award for AI-Powered Radiograph Education | School of Dental Medicine". www.dental.pitt.edu. Retrieved 2026-02-05.
  7. ^ a b c "Artificial Intelligence in Clinical Care: How Dentists are Using AI to Improve Diagnostics and Patient Communication". Oral Health Group. 2022-12-08. Retrieved 2026-02-05.
  8. ^ Kumar, Anuj; Bhadauria, Harvendra Singh; Singh, Annapurna (2021). "Descriptive analysis of dental X-ray images using various practical methods: A review". PeerJ Computer Science. 7 e620. doi:10.7717/peerj-cs.620. ISSN 2376-5992. PMC 8459782. PMID 34616881.
  9. ^ Pethani, Farhana; Dunn, Adam G. (2023-02-01). "Natural language processing for clinical notes in dentistry: A systematic review". Journal of Biomedical Informatics. 138 104282. doi:10.1016/j.jbi.2023.104282. ISSN 1532-0464. PMID 36623780.
  10. ^ Büttner, Martha; Leser, Ulf; Schneider, Lisa; Schwendicke, Falk (2024-02-01). "Natural Language Processing: Chances and Challenges in Dentistry". Journal of Dentistry. 141 104796. doi:10.1016/j.jdent.2023.104796. ISSN 0300-5712. PMID 38072335.
  11. ^ Büttner, Martha; Schwendicke, Falk (2023-05-01). "Natural language processing in dentistry". British Dental Journal. 234 (10): 753. doi:10.1038/s41415-023-5854-1. ISSN 1476-5373. PMID 37237206.
  12. ^ Albano, Domenico; Galiano, Vanessa; Basile, Mariachiara; Di Luca, Filippo; Gitto, Salvatore; Messina, Carmelo; Cagetti, Maria Grazia; Del Fabbro, Massimo; Tartaglia, Gianluca Martino; Sconfienza, Luca Maria (2024-02-24). "Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review". BMC Oral Health. 24 (1): 274. doi:10.1186/s12903-024-04046-7. ISSN 1472-6831. PMC 10894487. PMID 38402191.
  13. ^ "Diagnosis and Treatment Planning Varies Greatly Between Dentists". Dentistry Today. 2020-12-22. Retrieved 2026-02-05.
  14. ^ a b "U.S. FDA 510(k) Database. "K210187 — Overjet Dental Assist". www.accessdata.fda.gov. Retrieved 2026-02-05.
  15. ^ a b Health, Center for Devices and Radiological (2025-12-05). "Artificial Intelligence-Enabled Medical Devices". FDA.
  16. ^ "FDA 510(k) Summary for K232384" (PDF). U.S. Food and Drug Administration. U.S. Department of Health and Human Services. Retrieved 2025-12-30.

See also

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