DOI:

10.37988/1811-153X_2026_1_126

Application of artificial intelligence in pediatric dentistry: a systematic review

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Authors

  • E.E. Maslak 1, Doctor of Science in Medicine, professor of the Pediatric dentistry Department
    ORCID: 0000-0003-2011-9714
  • B.O. Khudanov 2, 3, Doctor of Science in Medicine, president; professor of the Preventive dentistry Department
    ORCID: 0000-0003-2080-1975
  • A.A. Shkhagosheva 1, PhD in Medical Sciences, associate professor of the Pediatric dentistry Department
    ORCID: 0000-0003-2925-7662
  • N.V. Matvienko 1, PhD in Medical Sciences, associate professor of the Pediatric dentistry Department
    ORCID: 0000-0002-3913-5725
  • N. Tuygunov 3, PhD student of the Preventive dentistry Department
    ORCID: 0009-0000-9781-1755
  • F.A. Abdurahimova 3, PhD in Medical Sciences, associate professor of the Preventive dentistry Department
    ORCID: 0009-0003-8113-2725
  • T.N. Kamennova 1, PhD in Medical Sciences, associate professor of the Pediatric dentistry Department
    ORCID: 0000-0002-1641-8159
  • O.A. Kostovinskaya 1, 5th year student at the Dental Faculty
    ORCID: 0009-0009-8932-7897
  • 1 Volgograd State Medical University, 400066, Volgograd, Russia
  • 2 Medical Institute “Impulse”, 111711, Chirchik, Uzbekistan
  • 3 Tashkent State Medical University, 100047, Tashkent, Uzbekistan

Abstract

The use of artificial intelligence in medicine is controversial. Objective: to synthesize and evaluate current evidence on AI use in pediatric dentistry.
Materials and methods.
A comprehensive literature search was conducted across PubMed/MEDLINE, eLibrary, etc. The PRISMA framework guided the selection process. Forty-one studies were included in the final analysis.
Results.
AI applications clustered into: radiographic analysis; intraoral photography interpretation; caries-risk prediction; and virtual assistants for decision support and education. Diagnostic efficiency of programs ranged broadly: accuracy 72—99%, sensitivity 20—100%, specificity 49—100%. Key barriers include heterogeneous datasets, limited real-world validation, and unresolved legal/ethical governance.
Conclusion.
AI can augment diagnostics, prevention, and training in pediatric dentistry, but should be considered as complement and not replace clinical judgment. Future work should prioritize large multicentre datasets, prospective clinical validation, and robust ethical—legal frameworks to ensure safe, equitable deployment AI in pediatric dentistry.

Key words:

artificial intelligence, pediatric dentistry, caries, radiograph analysis, training

For Citation

[1]
Maslak E.E., Khudanov B.O., Shkhagosheva A.A., Matvienko N.V., Tuygunov N., Abdurahimova F.A., Kamennova T.N., Kostovinskaya O.A. Application of artificial intelligence in pediatric dentistry: a systematic review. Clinical Dentistry (Russia).  2026; 29 (1): 126—135. DOI: 10.37988/1811-153X_2026_1_126

References

  1. Losev F.F., Sorokina A.A., Salakhov A.K., Dokin S.P. The use of artificial intelligence in modern dentistry in the Russian Federation. Stomatology. 2024; 5: 42—45 (In Russian). eLIBRARY ID: 74496094
  2. Gao S., Wang X., Xia Z., Zhang H., Yu J., Yang F. Artificial intelligence in dentistry: A narrative review of diagnostic and therapeutic applications. Med Sci Monit. 2025; 31: e946676. PMID: 40195079
  3. Obrubov A.A., Solovykh E.A., Nadtochiy A.G. Principles of operation of a computer program using artificial intelligence in maxillofacial radiology and assessment of its diagnostic effectiveness. Bulletin of Experimental Biology and Medicine. 2024; 12: 786—792 (In Russian). eLIBRARY ID: 76848180
  4. Samaranayake L., Tuygunov N., Schwendicke F., Osathanon T., Khurshid Z., Boymuradov S.A., Cahyanto A. The transformative role of artificial intelligence in dentistry: A comprehensive overview. Part 1: Fundamentals of AI, and its contemporary applications in dentistry. Int Dent J. 2025; 75 (2): 383—396. PMID: 40074616
  5. Tuygunov N., Samaranayake L., Khurshid Z., Rewthamrongsris P., Schwendicke F., Osathanon T., Yahya N.A. The transformative role of artificial intelligence in dentistry: A comprehensive overview. Part 2: The promise and perils, and the international dental federation communique. Int Dent J. 2025; 75 (2): 397—404. PMID: 40011130
  6. Oisieva K.Sh., Rozov R.A. Artificial intelligence in dentistry: A sign of the times. Stomatology. 2025; 1: 87—92 (In Russian). eLIBRARY ID: 80539436
  7. Tyagi M., Jain S., Ranjan M., Hassan S., Prakash N., Kumar D., Kumar A., Singh S. Artificial intelligence tools in dentistry: A systematic review on their application and outcomes. Cureus. 2025; 17 (5): e85062. PMID: 40585609
  8. Surdu A., Budala D.G., Luchian I., Foia L.G., Botnariu G.E., Scutariu M.M. Using AI in optimizing oral and dental diagnoses — A narrative review. Diagnostics (Basel). 2024; 14 (24): 2804. PMID: 39767164
  9. Naeimi S.M., Darvish S., Salman B.N., Luchian I. Artificial intelligence in adult and pediatric dentistry: A narrative review. Bioengineering (Basel). 2024; 11 (5): 431. PMID: 38790300
  10. La Rosa S., Quinzi V., Palazzo G., Ronsivalle V., Lo Giudice A. The implications of artificial intelligence in pedodontics: A scoping review of evidence-based literature. Healthcare (Basel). 2024; 12 (13): 1311. PMID: 38998846
  11. Kılıc M.C., Bayrakdar I.S., Çelik Ö., Bilgir E., Orhan K., Aydın O.B., Kaplan F.A., Sağlam H., Odabaş A., Aslan A.F., Yılmaz A.B. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Dentomaxillofac Radiol. 2021; 50 (6): 20200172. PMID: 33661699
  12. Bumann E.E., Al-Qarni S., Chandrashekar G., Sabzian R., Bohaty B., Lee Y. A novel collaborative learning model for mixed dentition and fillings segmentation in panoramic radiographs. J Dent. 2024; 140: 104779. PMID: 38007173
  13. Kaya E., et al. A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs. Imaging Sci Dent. 2022; 52 (3): 275—281. PMID: 36238699
  14. Kuwada C., Ariji Y., Fukuda M., Kise Y., Fujita H., Katsumata A., Ariji E. Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020; 130 (4): 464—469. PMID: 32507560
  15. Mine Y., Iwamoto Y., Okazaki S., Nakamura K., Takeda S., Peng T.Y., Mitsuhata C., Kakimoto N., Kozai K., Murayama T. Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study. Int J Paediatr Dent. 2022; 32 (5): 678—685. PMID: 34904304
  16. Ahn Y., Hwang J.J., Jung Y.H., Jeong T., Shin J. Automated mesiodens classification system using deep learning on panoramic radiographs of children. Diagnostics (Basel). 2021; 11 (8): 1477. PMID: 34441411
  17. Ha E.G., Jeon K.J., Kim Y.H., Kim J.Y., Han S.S. Automatic detection of mesiodens on panoramic radiographs using artificial intelligence. Sci Rep. 2021; 11 (1): 23061. PMID: 34845320
  18. Kim J., Hwang J.J., Jeong T., Cho B.H., Shin J. Deep learning-based identification of mesiodens using automatic maxillary anterior region estimation in panoramic radiography of children. Dentomaxillofac Radiol. 2022; 51 (7): 20210528. PMID: 35731733
  19. Wu T.J., Tsai C.L., Gao Q.Z., Chen Y.P., Kuo C.F., Huang Y.H. The application of artificial-intelligence-assisted dental age assessment in children with growth delay. J Pers Med. 2022; 12 (7): 1158. PMID: 35887655
  20. Zaborowicz M., Zaborowicz K., Biedziak B., Garbowski T. Deep learning neural modelling as a precise method in the assessment of the chronological age of children and adolescents using tooth and bone parameters. Sensors (Basel). 2022; 22 (2): 637. PMID: 35062599
  21. Wang J., Dou J., Han J., Li G., Tao J. A population-based study to assess two convolutional neural networks for dental age estimation. BMC Oral Health. 2023; 23 (1): 109. PMID: 36803132
  22. Li Z., Xiao N., Nan X., Chen K., Zhao Y., Wang S., Guo X., Gao C. Automatic dental age estimation in adolescents via oral panoramic imaging. Front Dent Med. 2025; 6: 1618246. PMID: 40642202
  23. Kurt A., Günaçar D.N., Şılbır F.Y., Yeşil Z., Bayrakdar İ.Ş., Çelik Ö., Bilgir E., Orhan K. Evaluation of tooth development stages with deep learning-based artificial intelligence algorithm. BMC Oral Health. 2024; 24 (1): 1034. PMID: 39227802
  24. Turosz N., Chęcińska K., Chęciński M., Lubecka K., Bliźniak F., Sikora M. Artificial intelligence (AI) assessment of pediatric dental panoramic radiographs (DPRs): A clinical study. Pediatr Rep. 2024; 16 (3): 794—805. PMID: 39311330
  25. Gonzalez C., Badr Z., Güngör H.C., Han S., Hamdan M.D. Identifying primary proximal caries lesions in pediatric patients from bitewing radiographs using artificial intelligence. Pediatr Dent. 2024; 46 (5): 332—336. PMID: 39420489
  26. Goertzen E., Casas M.J., Barrett E.J., Perschbacher S., Pusic M., Boutis K. Interactive computer-assisted learning as an educational method for learning pediatric interproximal dental caries identification. Oral Surg Oral Med Oral Pathol Oral Radiol. 2023; 136 (3): 371—381. PMID: 37271610
  27. Ghorbani Z., Mirebeigi-Jamasbi S.S., Hassannia Dargah M., Nahvi M., Hosseinikhah Manshadi S.A., Akbarzadeh Fathabadi Z. A novel deep learning-based model for automated tooth detection and numbering in mixed and permanent dentition in occlusal photographs. BMC Oral Health. 2025; 25 (1): 455. PMID: 40158107
  28. Li R.Z., Zhu J.X., Wang Y.Y., Zhao S.Y., Peng C.F., Zhou Q., Sun R.Q., Hao A.M., Li S., Wang Y., Xia B. [Development of a deep learning based prototype artificial intelligence system for the detection of dental caries in children]. Zhonghua Kou Qiang Yi Xue Za Zhi. 2021; 56 (12): 1253—1260 (In Chinese). PMID: 34915661
  29. Portella P.D., de Oliveira L.F., Ferreira M.F.C., Dias B.C., de Souza J.F., Assunção L.R.D.S. Improving accuracy of early dental carious lesions detection using deep learning-based automated method. Clin Oral Investig. 2023; 27 (12): 7663—7670. PMID: 37906303
  30. Alevizakos V., Bekes K., Steffen R., von See C. Artificial intelligence system for training diagnosis and differentiation with molar incisor hypomineralization (MIH) and similar pathologies. Clin Oral Investig. 2022; 26 (12): 6917—6923. PMID: 36065023
  31. Schönewolf J., Meyer O., Engels P., Schlickenrieder A., Hickel R., Gruhn V., Hesenius M., Kühnisch J. Artificial intelligence-based diagnostics of molar-incisor-hypomineralization (MIH) on intraoral photographs. Clin Oral Investig. 2022; 26 (9): 5923—5930. PMID: 35608684
  32. Felsch M., Meyer O., Schlickenrieder A., Engels P., Schönewolf J., Zöllner F., Heinrich-Weltzien R., Hesenius M., Hickel R., Gruhn V., Kühnisch J. Detection and localization of caries and hypomineralization on dental photographs with a vision transformer model. NPJ Digit Med. 2023; 6 (1): 198. PMID: 37880375
  33. Al-Jallad N., Ly-Mapes O., Hao P., Ruan J., Ramesh A., Luo J., Wu T.T., Dye T., Rashwan N., Ren J., Jang H., Mendez L., Alomeir N., Bullock S., Fiscella K., Xiao J. Artificial intelligence-powered smartphone application, AICaries, improves at-home dental caries screening in children: Moderated and unmoderated usability test. PLOS Digit Health. 2022; 1 (6): e0000046. PMID: 36381137
  34. Sobrinho B.P., Silva B.P., Andrade K.M., Sobrinho B.P., Ribeiro D.A., Santos J.N., Oliveira L.R., Cury P.R. Intelligent biofilm detection with ensemble of deep learning networks. Med Oral Patol Oral Cir Bucal. 2025; 30 (2): e282-e287. PMID: 39954282
  35. You W., Hao A., Li S., Wang Y., Xia B. Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments. BMC Oral Health. 2020; 20 (1): 141. PMID: 32404094
  36. Yüksel B., Özveren N., Yeşil Ç. Evaluation of dental plaque area with artificial intelligence model. Niger J Clin Pract. 2024; 27 (6): 759—765. PMID: 38943301
  37. Tez B.Ç., Güzel Y., Kızıltan Eliaçık B.B., Aydın Z. Deep-learning-based AI-model for predicting dental plaque in the young permanent teeth of children aged 8—13 years. Children (Basel). 2025; 12 (4): 475. PMID: 40310101
  38. Karhade D.S., Roach J., Shrestha P., Simancas-Pallares M.A., Ginnis J., Burk Z.J.S., Ribeiro A.A., Cho H., Wu D., Divaris K. An automated machine learning classifier for early childhood caries. Pediatr Dent. 2021; 43 (3): 191—197. PMID: 34172112
  39. Park Y.H., Kim S.H., Choi Y.Y. Prediction models of early childhood caries based on machine learning algorithms. Int J Environ Res Public Health. 2021; 18 (16): 8613. PMID: 34444368
  40. Ramos-Gomez F., Marcus M., Maida C.A., Wang Y., Kinsler J.J., Xiong D., Lee S.Y., Hays R.D., Shen J., Crall J.J., Liu H. Using a machine learning algorithm to predict the likelihood of presence of dental caries among children aged 2 to 7. Dent J (Basel). 2021; 9 (12): 141. PMID: 34940038
  41. Bogdan-Andreescu C.F., Defta C.L., Albu S.D., Manea A., Botoaca O., Russu E.A., Albu C.C. Application of artificial intelligence in dental caries prediction related to diet and oral hygiene. Romanian Journal of Oral Rehabilitation. 2024; 2: 48—55. DOI: 10.62610/RJOR.2024.2.16.5
  42. Wang Y., Hays R.D., Marcus M., Maida C.A., Shen J., Xiong D., Coulter I.D., Lee S.Y., Spolsky V.W., Crall J.J., Liu H. Developing children›s oral health assessment toolkits using machine learning algorithm. JDR Clin Trans Res. 2020; 5 (3): 233—243. PMID: 31710817
  43. Toledo Reyes L., Knorst J.K., Ortiz F.R., Brondani B., Emmanuelli B., Saraiva Guedes R., Mendes F.M., Ardenghi T.M. Early childhood predictors for dental caries: A machine learning approach. J Dent Res. 2023; 102 (9): 999—1006. PMID: 37246832
  44. Koopaie M., Salamati M., Montazeri R., Davoudi M., Kolahdooz S. Salivary cystatin S levels in children with early childhood caries in comparison with caries-free children; statistical analysis and machine learning. BMC Oral Health. 2021; 21 (1): 650. PMID: 34922509
  45. Raksakmanut R., Thanyasrisung P., Sritangsirikul S., Kitsahawong K., Seminario A.L., Pitiphat W., Matangkasombut O. Prediction of future caries in 1-year-old children via the salivary microbiome. J Dent Res. 2023; 102 (6): 626—635. PMID: 36919874
  46. Zaorska K., Szczapa T., Borysewicz-Lewicka M., Nowicki M., Gerreth K. Prediction of early childhood caries based on single nucleotide polymorphisms using neural networks. Genes (Basel). 2021; 12 (4): 462. PMID: 33805090
  47. Pang L., Wang K., Tao Y., Zhi Q., Zhang J., Lin H. A new model for caries risk prediction in teenagers using a machine learning algorithm based on environmental and genetic factors. Front Genet. 2021; 12: 636867. PMID: 33777105
  48. Udod O.A., Voronina H.S., Ivchenkova O.Y. Application of neural network technologies in the dental caries forecast. Wiad Lek. 2020; 73 (7): 1499—1504. DOI: 10.36740/WLek202007135
  49. Sadegh-Zadeh S.A., Bagheri M., Saadat M. Decoding children dental health risks: a machine learning approach to identifying key influencing factors. Front Artif Intell. 2024; 7: 1392597. PMID: 38952410
  50. Al-Kaff A.A., et al. Minimally invasive techniques for managing dental caries in children: Efficacy, applications, and future directions. Cureus. 2025; 17 (7): e87450. PMID: 40772222
  51. Bhadila G.Y., Alhomied M., Mahmoud A., Farsi N.J. Accuracy of artificial intelligence in making diagnoses and treatment decisions in pediatric dentistry. Pediatr Dent. 2025; 47 (2): 73—78. PMID: 40296263
  52. Rokhshad R., et al. Accuracy and consistency of chatbots versus clinicians for answering pediatric dentistry questions: A pilot study. J Dent. 2024; 144: 104938. PMID: 38499280
  53. Gökcek Taraç M., Nale T. Artificial intelligence in pediatric dental trauma: do artificial intelligence chatbots address parental concerns effectively? —BMC Oral Health. 2025; 25 (1): 736. PMID: 40382588
  54. Barros Padilha D.X., Veiga N.J., Mello-Moura A.C.V., Nunes Correia P. Virtual reality and behaviour management in paediatric dentistry: a systematic review. BMC Oral Health. 2023; 23 (1): 995. PMID: 38087294
  55. Kasimoglu Y., Kocaaydin S., Karsli E., Esen M., Bektas I., Ince G., Tuna E.B. Robotic approach to the reduction of dental anxiety in children. Acta Odontol Scand. 2020; 78 (6): 474—480. PMID: 32730719
  56. Acharya S., Godhi B.S., Saxena V., Assiry A.A., Alessa N.A., Dawasaz A.A., Alqarni A., Karobari M.I. Role of artificial intelligence in behavior management of pediatric dental patients-a mini review. J Clin Pediatr Dent. 2024; 48 (3): 24—30. PMID: 38755978
  57. Rokhshad R., et al. Current applications of artificial intelligence for pediatric dentistry: A systematic review and meta-analysis. Pediatr Dent. 2024; 46 (1): 27—35. PMID: 38449036
  58. Kliman Y.A. Legal Issues of applying artificial intelligence in healthcare. Theory and Practice of Social Development. 2024; 11 (199): 237—243 (In Russian). eLIBRARY ID: 75138179
  59. Ducret M., Mörch C.M. Focus on artificial intelligence ethics in dentistry. J Dent Sci. 2023; 18 (3): 1409—1410. PMID: 37404652
  60. Shah M., Ali S.M., Batool R., Shafiq F., Shaikh G.M., Khero R. The limitless potential of artificial intelligence in paediatric dentistry. Journal of Health and Rehabilitation Research. 2024; 4 (3). DOI: 10.61919/jhrr.v4i3.1540

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Received

November 4, 2025

Accepted

January 16, 2026

Published on

March 31, 2026