Application and Challenges of Artificial Intelligence in Different Branches of Dentistry
Abstract
Introduction: Artificial Intelligence (AI) is transforming dental practice through its ability to analyze, learn, and support clinical decisions. By enhancing diagnostic precision and treatment planning, AI tools are revolutionizing patient care. Despite its potential, implementation faces obstacles including ethical considerations and algorithmic limitations. This review examines AI applications across dental specialties.Materials and Methods: This narrative review was conducted by analyzing recent studies on AI applications in dentistry. The literature was sourced from reputable databases, including PubMed and Scopus, focusing on AI-driven diagnostic and therapeutic advancements in oral and maxillofacial surgery, radiology, restorative dentistry, orthodontics, periodontics, endodontics, prosthodontics, and forensic dentistry. Results: AI demonstrates remarkable capabilities across dental fields. Deep learning systems excel in detecting caries, periapical lesions, and fractures through radiological analysis. Orthodontic applications include automated cephalometric analysis and treatment simulation. In restorative dentistry, AI enhances cavity detection and restoration assessment. Maxillofacial applications include surgical outcome prediction and pathology identification. Forensic applications facilitate age and gender determination through radiographic analysis. Current challenges include data security, algorithmic bias, and ethical compliance.Conclusion: While AI shows promise in advancing dental diagnostics and treatment accuracy, successful clinical integration requires addressing privacy concerns, establishing regulatory standards, and developing comprehensive professional training programs. Keywords: Artificial intelligence; Dentistry; Diagnostic imaging; Machine learning; Digital dentistry.
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7. Akst J. A primer: artificial intelligence versus neural networks. Inspiring Innovation: The Scientist Exploring Life. 2019;65802.
8. Rabunal JR, Dorado J. Artificial neural networks in real-life applications: IGI Global; 2006.
9. Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointestinal endoscopy. 2020;92(4):807-12.
10. Hung M, Voss MW, Rosales MN, Li W, Su W, Xu J, et al. Application of machine learning for diagnostic prediction of root caries. Gerodontology. 2019;36(4):395-404.
11. Murata M, Ariji Y, Ohashi Y, Kawai T, Fukuda M, Funakoshi T, et al. Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral radiology. 2019;35(3):301-7.
12. Yang H, Jo E, Kim HJ, Cha I-h, Jung Y-S, Nam W, et al. Deep learning for automated detection of cyst and tumors of the jaw in panoramic radiographs. Journal of clinical medicine. 2020;9(6):1839.
13. Ariji Y, Fukuda M, Kise Y, Nozawa M, Yanashita Y, Fujita H, et al. Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence. Oral surgery, oral medicine, oral pathology and oral radiology. 2019;127(5):458-63.
14. Zhang W, Li J, Li Z-B, Li Z. Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation. Scientific reports. 2018;8(1):1-9.
15. Kim BS, Yeom HG, Lee JH, Shin WS, Yun JP, Jeong SH, et al. Deep Learning-Based Prediction of Paresthesia after Third Molar Extraction: A Preliminary Study. Diagnostics. 2021;11(9):1572.
16. Lim J, Tanikawa C, Kogo M, Yamashiro T. Determination of prognostic factors for orthognathic surgery in children with cleft lip and/or palate. Orthodontics & Craniofacial Research. 2021;24:153-62.
17. Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, et al. Deep learning for the radiographic detection of periodontal bone loss. Scientific reports. 2019;9(1):1-6.
18. Cha J-Y, Yoon H-I, Yeo I-S, Huh K-H, Han J-S. Peri-implant bone loss measurement using a region-based convolutional neural network on dental periapical radiographs. Journal of clinical medicine. 2021;10(5):1009.
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22. Boreak N. Effectiveness of Artificial Intelligence Applications Designed for Endodontic Diagnosis, Decision-making, and Prediction of Prognosis: A Systematic Review. The Journal of Contemporary Dental Practice. 2020;21(8):926-34.
23. Endres MG, Hillen F, Salloumis M, Sedaghat AR, Niehues SM, Quatela O, et al. Development of a deep learning algorithm for periapical disease detection in dental radiographs. Diagnostics. 2020;10(6):430.
24. Kök H, Acilar AM, İzgi MS. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Progress in Orthodontics. 2019;20(1):1-10.
25. Arik SÖ, Ibragimov B, Xing L. Fully automated quantitative cephalometry using convolutional neural networks. Journal of Medical Imaging. 2017;4(1):014501.
26. Xie X, Wang L, Wang A. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. The Angle Orthodontist. 2010;80(2):262-6.
27. Tanny L, Huang B, Shaweesh A, Currie G. Characterisation of anterior open bite in primary school‐aged children: A preliminary study with artificial neural network analysis. International Journal of Paediatric Dentistry. 2021;31(5):576-82.
28. Ahn Y, Hwang JJ, Jung Y-H, Jeong T, Shin J. Automated Mesiodens Classification System Using Deep Learning on Panoramic Radiographs of Children. Diagnostics. 2021;11(8):1477.
29. Kong H-J, Kim Y-L. Application of artificial intelligence in dental crown prosthesis: a scoping review. BMC Oral Health. 2024;24(1):937.
30. De Tobel J, Radesh P, Vandermeulen D, Thevissen PW. An automated technique to stage lower third molar development on panoramic radiographs for age estimation: a pilot study. The Journal of forensic odonto-stomatology. 2017;35(2):42.
31. Patil V, Vineetha R, Vatsa S, Shetty DK, Raju A, Naik N, et al. Artificial neural network for gender determination using mandibular morphometric parameters: A comparative retrospective study. Cogent Engineering. 2020;7(1):1723783.
32. Heinrich A, Güttler F, Wendt S, Schenkl S, Hubig M, Wagner R, et al., editors. Forensic odontology: automatic identification of persons comparing antemortem and postmortem panoramic radiographs using computer vision. RöFo-Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren; 2018: © Georg Thieme Verlag KG.
33. Gamulin O, Škrabić M, Serec K, Par M, Baković M, Krajačić M, et al. Possibility of Human Gender Recognition Using Raman Spectra of Teeth. Molecules. 2021;26(13):3983.
34. Nguyen TT, Larrivée N, Lee A, Bilaniuk O, Durand R. Use of artificial intelligence in dentistry: current clinical trends and research advances. J Can Dent Assoc. 2021;87(l7):1488-2159.
35. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36-S40.
36. Haag M, Igel C, Fischer MR, GMA GMES. Digital Teaching and Digital Medicine: A national initiative is needed. GMS journal for medical education. 2018;35(3).
37. Chaiyachati KH, Shea JA, Asch DA, Liu M, Bellini LM, Dine CJ, et al. Assessment of inpatient time allocation among first-year internal medicine residents using time-motion observations. JAMA internal medicine. 2019;179(6):760-7.
38. Shah NR. Health care in 2030: will artificial intelligence replace physicians? : American College of Physicians; 2019. p. 407-8.
39. Price WN, Gerke S, Cohen IG. Potential liability for physicians using artificial intelligence. Jama. 2019;322(18):1765-6.
40. Ghods K, Azizi A, Jafari A, Ghods K. Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature. J Dent (Shiraz). 2023;24(4):356-71.
41. Alharbi SS, Alhasson HF. Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review. Diagnostics (Basel). 2024;14(21).
42. Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofacial Radiology. 2020;49(1):20190107.
43. Mahdi FP, Motoki K, Kobashi S. Optimization technique combined with deep learning method for teeth recognition in dental panoramic radiographs. Scientific Reports. 2020;10(1):1-12.
44. Devito KL, de Souza Barbosa F, Felippe Filho WN. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology. 2008;106(6):879-84.
45. Lee K-S, Jung S-K, Ryu J-J, Shin S-W, Choi J. Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs. Journal of clinical medicine. 2020;9(2):392.
46. Kise Y, Ikeda H, Fujii T, Fukuda M, Ariji Y, Fujita H, et al. Preliminary study on the application of deep learning system to diagnosis of Sjögren's syndrome on CT images. Dentomaxillofacial Radiology. 2019;48(6):20190019.
47. Shujaat S. Automated Machine Learning in Dentistry: A Narrative Review of Applications, Challenges, and Future Directions. Diagnostics. 2025;15(3):273.
48. Musleh D, Almossaeed H, Balhareth F, Alqahtani G, Alobaidan N, Altalag J, et al. Advancing Dental Diagnostics: A Review of Artificial Intelligence Applications and Challenges in Dentistry. Big Data and Cognitive Computing. 2024;8(6):66.
49. Revilla-León M, Gómez-Polo M, Vyas S, Barmak AB, Özcan M, Att W, et al. Artificial intelligence applications in restorative dentistry: A systematic review. The Journal of prosthetic dentistry. 2021.
50. Schlickenrieder A, Meyer O, Schönewolf J, Engels P, Hickel R, Gruhn V, et al. Automatized Detection and Categorization of Fissure Sealants from Intraoral Digital Photographs Using Artificial Intelligence. Diagnostics. 2021;11(9):1608.
51. Aubreville M, Knipfer C, Oetter N, Jaremenko C, Rodner E, Denzler J, et al. Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning. Scientific reports. 2017;7(1):1-10.
52. Ossowska A, Kusiak A, Świetlik D. Artificial Intelligence in Dentistry—Narrative Review. International Journal of Environmental Research and Public Health. 2022;19(6):3449.
53. Kim DW, Kim H, Nam W, Kim HJ, Cha I-H. Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: a preliminary report. Bone. 2018;116:207-14.
54. Nishiyama M, Ishibashi K, Ariji Y, Fukuda M, Nishiyama W, Umemura M, et al. Performance of deep learning models constructed using panoramic radiographs from two hospitals to diagnose fractures of the mandibular condyle. Dentomaxillofacial Radiology. 2021;50(7):20200611.
55. Leonardi R, Giordano D, Maiorana F. An evaluation of cellular neural networks for the automatic identification of cephalometric landmarks on digital images. Journal of Biomedicine and Biotechnology. 2009;2009.
56. de Jesus VC, Khan MW, Mittermuller B-A, Duan K, Hu P, Schroth RJ, et al. Characterization of Supragingival Plaque and Oral Swab Microbiomes in Children With Severe Early Childhood Caries. Frontiers in Microbiology. 2021:1517.
57. Umer F, Adnan N. Generative artificial intelligence: synthetic datasets in dentistry. BDJ Open. 2024;10(1):13.
58. Ding H, Wu J, Zhao W, Matinlinna JP, Burrow MF, Tsoi JKH. Artificial intelligence in dentistry—A review. Frontiers in Dental Medicine. 2023;4.
59. Hwang J-J, Jung Y-H, Cho B-H, Heo M-S. An overview of deep learning in the field of dentistry. Imaging science in dentistry. 2019;49(1):1-7.
60. Zhang K, Wu J, Chen H, Lyu P. An effective teeth recognition method using label tree with cascade network structure. Computerized Medical Imaging and Graphics. 2018;68:61-70.
61. Uribe SE, Issa J, Sohrabniya F, Denny A, Kim NN, Dayo AF, et al. Publicly Available Dental Image Datasets for Artificial Intelligence. Journal of Dental Research. 2024;103(13):1365-74.
62. Feher B, Tussie C, Giannobile WV. Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches. Frontiers in Artificial Intelligence. 2024;7.
2. Rajaraman V. JohnMcCarthy—Father of artificial intelligence. Resonance. 2014;19(3):198-207.
3. Amisha PM, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. Journal of family medicine and primary care. 2019;8(7):2328.
4. Khanagar SB, Al-Ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, et al. Developments, application, and performance of artificial intelligence in dentistry–A systematic review. Journal of dental sciences. 2021;16(1):508-22.
5. Russell S, Norvig P. Artificial intelligence: a modern approach. 2002.
6. Tiwari A, Kumar A, Jain S, Dhull KS, Sajjanar A, Puthenkandathil R, et al. Implications of ChatGPT in Public Health Dentistry: A Systematic Review. Cureus. 2023;15(6):e40367.
7. Akst J. A primer: artificial intelligence versus neural networks. Inspiring Innovation: The Scientist Exploring Life. 2019;65802.
8. Rabunal JR, Dorado J. Artificial neural networks in real-life applications: IGI Global; 2006.
9. Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointestinal endoscopy. 2020;92(4):807-12.
10. Hung M, Voss MW, Rosales MN, Li W, Su W, Xu J, et al. Application of machine learning for diagnostic prediction of root caries. Gerodontology. 2019;36(4):395-404.
11. Murata M, Ariji Y, Ohashi Y, Kawai T, Fukuda M, Funakoshi T, et al. Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral radiology. 2019;35(3):301-7.
12. Yang H, Jo E, Kim HJ, Cha I-h, Jung Y-S, Nam W, et al. Deep learning for automated detection of cyst and tumors of the jaw in panoramic radiographs. Journal of clinical medicine. 2020;9(6):1839.
13. Ariji Y, Fukuda M, Kise Y, Nozawa M, Yanashita Y, Fujita H, et al. Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence. Oral surgery, oral medicine, oral pathology and oral radiology. 2019;127(5):458-63.
14. Zhang W, Li J, Li Z-B, Li Z. Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation. Scientific reports. 2018;8(1):1-9.
15. Kim BS, Yeom HG, Lee JH, Shin WS, Yun JP, Jeong SH, et al. Deep Learning-Based Prediction of Paresthesia after Third Molar Extraction: A Preliminary Study. Diagnostics. 2021;11(9):1572.
16. Lim J, Tanikawa C, Kogo M, Yamashiro T. Determination of prognostic factors for orthognathic surgery in children with cleft lip and/or palate. Orthodontics & Craniofacial Research. 2021;24:153-62.
17. Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, et al. Deep learning for the radiographic detection of periodontal bone loss. Scientific reports. 2019;9(1):1-6.
18. Cha J-Y, Yoon H-I, Yeo I-S, Huh K-H, Han J-S. Peri-implant bone loss measurement using a region-based convolutional neural network on dental periapical radiographs. Journal of clinical medicine. 2021;10(5):1009.
19. Khanagar SB, Al-Ehaideb A, Vishwanathaiah S, Maganur PC, Patil S, Naik S, et al. Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making-a systematic review. Journal of dental sciences. 2021;16(1):482-92.
20. Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, et al. Deep learning for the radiographic detection of apical lesions. Journal of endodontics. 2019;45(7):917-22. e5.
21. Johari M, Esmaeili F, Andalib A, Garjani S, Saberkari H. Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an ex vivo study. Dentomaxillofacial Radiology. 2017;46(2):20160107.
22. Boreak N. Effectiveness of Artificial Intelligence Applications Designed for Endodontic Diagnosis, Decision-making, and Prediction of Prognosis: A Systematic Review. The Journal of Contemporary Dental Practice. 2020;21(8):926-34.
23. Endres MG, Hillen F, Salloumis M, Sedaghat AR, Niehues SM, Quatela O, et al. Development of a deep learning algorithm for periapical disease detection in dental radiographs. Diagnostics. 2020;10(6):430.
24. Kök H, Acilar AM, İzgi MS. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Progress in Orthodontics. 2019;20(1):1-10.
25. Arik SÖ, Ibragimov B, Xing L. Fully automated quantitative cephalometry using convolutional neural networks. Journal of Medical Imaging. 2017;4(1):014501.
26. Xie X, Wang L, Wang A. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. The Angle Orthodontist. 2010;80(2):262-6.
27. Tanny L, Huang B, Shaweesh A, Currie G. Characterisation of anterior open bite in primary school‐aged children: A preliminary study with artificial neural network analysis. International Journal of Paediatric Dentistry. 2021;31(5):576-82.
28. Ahn Y, Hwang JJ, Jung Y-H, Jeong T, Shin J. Automated Mesiodens Classification System Using Deep Learning on Panoramic Radiographs of Children. Diagnostics. 2021;11(8):1477.
29. Kong H-J, Kim Y-L. Application of artificial intelligence in dental crown prosthesis: a scoping review. BMC Oral Health. 2024;24(1):937.
30. De Tobel J, Radesh P, Vandermeulen D, Thevissen PW. An automated technique to stage lower third molar development on panoramic radiographs for age estimation: a pilot study. The Journal of forensic odonto-stomatology. 2017;35(2):42.
31. Patil V, Vineetha R, Vatsa S, Shetty DK, Raju A, Naik N, et al. Artificial neural network for gender determination using mandibular morphometric parameters: A comparative retrospective study. Cogent Engineering. 2020;7(1):1723783.
32. Heinrich A, Güttler F, Wendt S, Schenkl S, Hubig M, Wagner R, et al., editors. Forensic odontology: automatic identification of persons comparing antemortem and postmortem panoramic radiographs using computer vision. RöFo-Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren; 2018: © Georg Thieme Verlag KG.
33. Gamulin O, Škrabić M, Serec K, Par M, Baković M, Krajačić M, et al. Possibility of Human Gender Recognition Using Raman Spectra of Teeth. Molecules. 2021;26(13):3983.
34. Nguyen TT, Larrivée N, Lee A, Bilaniuk O, Durand R. Use of artificial intelligence in dentistry: current clinical trends and research advances. J Can Dent Assoc. 2021;87(l7):1488-2159.
35. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36-S40.
36. Haag M, Igel C, Fischer MR, GMA GMES. Digital Teaching and Digital Medicine: A national initiative is needed. GMS journal for medical education. 2018;35(3).
37. Chaiyachati KH, Shea JA, Asch DA, Liu M, Bellini LM, Dine CJ, et al. Assessment of inpatient time allocation among first-year internal medicine residents using time-motion observations. JAMA internal medicine. 2019;179(6):760-7.
38. Shah NR. Health care in 2030: will artificial intelligence replace physicians? : American College of Physicians; 2019. p. 407-8.
39. Price WN, Gerke S, Cohen IG. Potential liability for physicians using artificial intelligence. Jama. 2019;322(18):1765-6.
40. Ghods K, Azizi A, Jafari A, Ghods K. Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature. J Dent (Shiraz). 2023;24(4):356-71.
41. Alharbi SS, Alhasson HF. Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review. Diagnostics (Basel). 2024;14(21).
42. Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofacial Radiology. 2020;49(1):20190107.
43. Mahdi FP, Motoki K, Kobashi S. Optimization technique combined with deep learning method for teeth recognition in dental panoramic radiographs. Scientific Reports. 2020;10(1):1-12.
44. Devito KL, de Souza Barbosa F, Felippe Filho WN. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology. 2008;106(6):879-84.
45. Lee K-S, Jung S-K, Ryu J-J, Shin S-W, Choi J. Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs. Journal of clinical medicine. 2020;9(2):392.
46. Kise Y, Ikeda H, Fujii T, Fukuda M, Ariji Y, Fujita H, et al. Preliminary study on the application of deep learning system to diagnosis of Sjögren's syndrome on CT images. Dentomaxillofacial Radiology. 2019;48(6):20190019.
47. Shujaat S. Automated Machine Learning in Dentistry: A Narrative Review of Applications, Challenges, and Future Directions. Diagnostics. 2025;15(3):273.
48. Musleh D, Almossaeed H, Balhareth F, Alqahtani G, Alobaidan N, Altalag J, et al. Advancing Dental Diagnostics: A Review of Artificial Intelligence Applications and Challenges in Dentistry. Big Data and Cognitive Computing. 2024;8(6):66.
49. Revilla-León M, Gómez-Polo M, Vyas S, Barmak AB, Özcan M, Att W, et al. Artificial intelligence applications in restorative dentistry: A systematic review. The Journal of prosthetic dentistry. 2021.
50. Schlickenrieder A, Meyer O, Schönewolf J, Engels P, Hickel R, Gruhn V, et al. Automatized Detection and Categorization of Fissure Sealants from Intraoral Digital Photographs Using Artificial Intelligence. Diagnostics. 2021;11(9):1608.
51. Aubreville M, Knipfer C, Oetter N, Jaremenko C, Rodner E, Denzler J, et al. Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning. Scientific reports. 2017;7(1):1-10.
52. Ossowska A, Kusiak A, Świetlik D. Artificial Intelligence in Dentistry—Narrative Review. International Journal of Environmental Research and Public Health. 2022;19(6):3449.
53. Kim DW, Kim H, Nam W, Kim HJ, Cha I-H. Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: a preliminary report. Bone. 2018;116:207-14.
54. Nishiyama M, Ishibashi K, Ariji Y, Fukuda M, Nishiyama W, Umemura M, et al. Performance of deep learning models constructed using panoramic radiographs from two hospitals to diagnose fractures of the mandibular condyle. Dentomaxillofacial Radiology. 2021;50(7):20200611.
55. Leonardi R, Giordano D, Maiorana F. An evaluation of cellular neural networks for the automatic identification of cephalometric landmarks on digital images. Journal of Biomedicine and Biotechnology. 2009;2009.
56. de Jesus VC, Khan MW, Mittermuller B-A, Duan K, Hu P, Schroth RJ, et al. Characterization of Supragingival Plaque and Oral Swab Microbiomes in Children With Severe Early Childhood Caries. Frontiers in Microbiology. 2021:1517.
57. Umer F, Adnan N. Generative artificial intelligence: synthetic datasets in dentistry. BDJ Open. 2024;10(1):13.
58. Ding H, Wu J, Zhao W, Matinlinna JP, Burrow MF, Tsoi JKH. Artificial intelligence in dentistry—A review. Frontiers in Dental Medicine. 2023;4.
59. Hwang J-J, Jung Y-H, Cho B-H, Heo M-S. An overview of deep learning in the field of dentistry. Imaging science in dentistry. 2019;49(1):1-7.
60. Zhang K, Wu J, Chen H, Lyu P. An effective teeth recognition method using label tree with cascade network structure. Computerized Medical Imaging and Graphics. 2018;68:61-70.
61. Uribe SE, Issa J, Sohrabniya F, Denny A, Kim NN, Dayo AF, et al. Publicly Available Dental Image Datasets for Artificial Intelligence. Journal of Dental Research. 2024;103(13):1365-74.
62. Feher B, Tussie C, Giannobile WV. Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches. Frontiers in Artificial Intelligence. 2024;7.
| Files | ||
| Issue | Vol 11, No 4 (Autumn 2024) | |
| Section | Original Article(s) | |
| Keywords | ||
| Artificial intelligence; Dentistry; Diagnostic imaging; Machine learning; Digital dentistry. | ||
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How to Cite
1.
Mirzaei A, Mollaei M, Lotfizadeh A, Aryana M, Ebrahimpour A. Application and Challenges of Artificial Intelligence in Different Branches of Dentistry. J Craniomaxillofac Res. 2025;11(4):215-222.
