The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Developments, application, and performance of artificial intelligence in dentistry – A systematic review
Open accessAbstract
BACKGROUND/PURPOSE: Artificial intelligence (AI) has made deep inroads into dentistry in the last few years. The aim of this systematic review was to identify the development of AI applications that are widely employed in dentistry and evaluate their performance in terms of diagnosis, clinical decision-making, and predicting the prognosis of the treatment. MATERIALS AND METHODS: The literature for this paper was identified and selected by performing a thorough search in the electronic data bases like PubMed, Medline, Embase, Cochrane, Google scholar, Scopus, Web of science, and Saudi digital library published over the past two decades (January 2000-March 15, 2020).After applying inclusion and exclusion criteria, 43 articles were read in full and critically analyzed. Quality analysis was performed using QUADAS-2. RESULTS: AI technologies are widely implemented in a wide range of dentistry specialties. Most of the documented work is focused on AI models that rely on convolutional neural networks (CNNs) and artificial neural networks (ANNs). These AI models have been used in detection and diagnosis of dental caries, vertical root fractures, apical lesions, salivary gland diseases, maxillary sinusitis, maxillofacial cysts, cervical lymph nodes metastasis, osteoporosis, cancerous lesions, alveolar bone loss, predicting orthodontic extractions, need for orthodontic treatments, cephalometric analysis, age and gender determination. CONCLUSION: These studies indicate that the performance of an AI based automated system is excellent. They mimic the precision and accuracy of trained specialists, in some studies it was found that these systems were even able to outmatch dental specialists in terms of performance and accuracy.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies
Whiting, P. F., Rutjes, A. W. S., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., Leeflang, M. M. G., Sterne, J. A. C., Bossuyt, P. M. M., & the QUADAS-2 Group* (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-8-201110180-00009 · Cited by 14,199
Empirical Evaluation of Rectified Activations in Convolutional Network
Xu, B., Wang, N., Chen, T., & Li, M. (2015) · arXiv · doi:10.48550/arxiv.1505.00853 · Cited by 2,264
Contemporary Orthodontics (2012)
McDonald, F. (2013) · The European Journal of Orthodontics · doi:10.1093/ejo/cjs071 · Cited by 2,131
Lee, J.-H., Kim, D.-H., Jeong, S.-N., & Choi, S.-H. (2018) · Journal of Dentistry · doi:10.1016/j.jdent.2018.07.015 · Cited by 1,002
Facial keys to orthodontic diagnosis and treatment planning. Part I
Arnett, G. W., & Bergman, R. T. (1993) · American Journal of Orthodontics and Dentofacial Orthopedics · doi:10.1016/0889-5406(93)70010-l · Cited by 819
Deep Learning for the Radiographic Detection of Periodontal Bone Loss
Krois, J., Ekert, T., Meinhold, L., Golla, T., Kharbot, B., Wittemeier, A., Dörfer, C., & Schwendicke, F. (2019) · Scientific Reports · doi:10.1038/s41598-019-44839-3 · Cited by 517
Lee, J.-H., Kim, D.-H., Jeong, S.-N., & Choi, S.-H. (2018) · Journal of Periodontal & Implant Science · doi:10.5051/jpis.2018.48.2.114 · Cited by 502
Tooth detection and numbering in panoramic radiographs using convolutional neural networks
Tuzoff, D. V., Tuzova, L. N., Bornstein, M. M., Krasnov, A. S., Kharchenko, M. A., Nikolenko, S. I., Sveshnikov, M. M., & Bednenko, G. B. (2019) · Dentomaxillofacial Radiology · doi:10.1259/dmfr.20180051 · Cited by 487
Maturation indicators and the pubertal growth spurt
Hägg, U., & Taranger, J. (1982) · American Journal of Orthodontics · doi:10.1016/0002-9416(82)90464-x · Cited by 452
Deep Learning for the Radiographic Detection of Apical Lesions
Ekert, T., Krois, J., Meinhold, L., Elhennawy, K., Emara, R., Golla, T., & Schwendicke, F. (2019) · Journal of Endodontics · doi:10.1016/j.joen.2019.03.016 · Cited by 384
Cited by
Artificial intelligence in dentistry-a review
Ding, H., Wu, J., Zhao, W., Matinlinna, J. P., Burrow, M. F., & Tsoi, J. K. H. (2023) · Frontiers in Dental Medicine · doi:10.3389/fdmed.2023.1085251 · Cited by 297
Eschert, T., Schwendicke, F., Krois, J., Bohner, L., Vinayahalingam, S., & Hanisch, M. (2022) · Medicina · doi:10.3390/medicina58081059 · Cited by 53
Applications of Artificial Intelligence in Dental Medicine: A Critical Review
Sitaras, S., Tsolakis, I. A., Gelsini, M., Tsolakis, A. I., Schwendicke, F., Wolf, T. G., & Perlea, P. (2025) · International Dental Journal · doi:10.1016/j.identj.2024.11.009 · Cited by 42
Metadata
- Authors
- Khanagar, S. B., Al-ehaideb, A., Maganur, P. C., Vishwanathaiah, S., Patil, S., Baeshen, H. A., Sarode, S. C., & Bhandi, S.
- Venue
- Journal of Dental Sciences
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 16
- Issue
- 1
- Pages
- 508-522
- ISSN
- 1991-7902 (ISSN-L)
- OpenAlex
- W3040455124
- Cited by
- 676
- References
- 57
- FWCI
- 19.18
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Developments, application, and performance of artificial intelligence in dentistry – A systematic review. https://app.cochat.ai/papers/10.1016/j.jds.2020.06.019
- The paper
- Khanagar, S. B., Al-ehaideb, A., Maganur, P. C., Vishwanathaiah, S., Patil, S., Baeshen, H. A., Sarode, S. C., & Bhandi, S. (2021). Developments, application, and performance of artificial intelligence in dentistry – A systematic review. Journal of Dental Sciences. https://doi.org/10.1016/j.jds.2020.06.019
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.