Artificial Intelligence–Driven Advancements in Dental Implants: A Narrative Review
Main Article Content
Abstract
We are in the era of advancements with digital innovative technologies, which have taken dental systems to the next level through the discovery of artificial intelligence (AI). Recently, AI systems have gained significant popularity in the field of prosthodontics, particularly in implant dentistry. Various studies have emphasized the impact of AI on oral implants, particularly in terms of diagnostic efficiency, treatment planning ability, and subsequent patient outcomes, thereby highlighting the accuracy, rapidity, and precision of decision-making. AI continues to expand in future and promises to amend the view of implant dentistry and lead it to an efficient and personalized part of the oral healthcare system. This review mainly focuses on the advancements of AI-based research in dental implantology and also explores the various applications of AI models. Inclusive data from databases in PubMed, Scopus, Web of Science, and Google Scholar were thoroughly explored, and the role of AI in implant identification, planning, prediction, and complication management was described. Although there are a few drawbacks present, which make AI a challenging factor for continued use in clinical practice. Further research with numerous clinical trials can break through the limitations and be used as an excellent supportive tool.
Article Details
Section

This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
1. Altalhi AM, Alharbi FS, Alhodaithy MA, Almarshedy BS, Al-Saaib MY, Aljohani AS, Alshareef AH, Muhayya M, AL-harbi NH. The Impact of Artificial Intelligence on Dental Implantology: A Narrative Review. Cureus. 2023;15(10):e47941. https://doi.org/10.7759/cureus.47941
2. Revilla-León M, Gómez-Polo M, Vyas S, Barmak BA, Galluci GO, Att W, Krishnamurthy VR. Artificial intelligence applications in implant dentistry: A systematic review. J Prosthet Dent. 2023;129(2):293-300. https://doi.org/10.1016/j.prosdent.2021.05.008
3. Saeed A, Alkhurays M, AlMutlaqah M, AlAzbah M, Alajlan SA. Future of using robotic and artificial intelligence in implant dentistry. Cureus. 2023;15(8):e43209. https://doi.org/10.7759/cureus.43209
4. Khan SF, Siddique A, Khan AM, Shetty B, Fazal I. Artificial intelligence in periodontology and implantology—a narrative review. J Med Artif Intelligen. 2024;7:6. https://doi.org/10.21037/jmai-23-186
5. Duggal N. What is Artificial Intelligence and Why It Matters in 2024? 2023. https://softcircles.com/blog/what-is-artificial-intelligence-and-why-it-matters-in-2024 Accessed 6.12.2024.
6. Ramani S, Vijayalakshmi R, Mahendra J, NalinaKumari CB, Ravi N. Artificial intelligence in periodontics–An overview. IP Int J Periodontol Implantol. 2023;8(2):71-4. https://doi.org/10.18231/j.ijpi.2023.015
7. Benakatti V, Nayakar RP, Patil S. Artificial intelligence applications in dental implantology: A narrative review. IP Annals Prosthodont Restorat Dent. 2024;10(2):118-23. https://doi.org/10.18231/j.aprd.2024.023
8. Shan T, Tay FR, Gu L. Application of artificial intelligence in Dentistry. J Dent Res. 2021;100(3):232–44. https://doi.org/10.1177/0022034520969115
9. Rutkowski JL. Artificial Intelligence (AI) Role in Implant Dentistry. J Oral Implantol. 2024;50(1):1-2. https://doi.org/10.1563/Editorial
10. Tandon D, Rajawat J, Banerjee M. Present and future of artificial intelligence in dentistry. J Oral Biol Craniofac Res. 2020;10(4):391-6. https://doi.org/10.1016/j.jobcr.2020.07.015
11. Geng, H. et al. Application of artificial intelligence in implant planning, placement, and prosthesis manufacturing: A systematic review. J Prosthodont, 2019;28(7);759-766.
12. Lee JH, Kim YT, Lee JB, Jeong SN. A Performance Comparison between Automated Deep Learning and Dental Professionals in Classification of Dental Implant Systems from Dental Imaging: A Multi-Center Study. Diagnostics (Basel). 2020;10(11):910. https://doi.org/10.3390/diagnostics10110910
13. Saïd MH, Roux MKL, Catherine JH, Lan R. Development of an artificial intelligence model to identify a dental implant from a radiograph. Int J Oral Maxillofac Implants. 2020;36(6): 1077–82. https://doi.org/10.11607/jomi.8060
14. Takahashi T, Nozaki K, Gonda T, Mameno T, Wada M, Ikebe K, et al. Identification of dental implants using deep learning -pilot study. Int J Implant Dent. 2020;6:53–9. https://doi.org/10.1186/s40729-020-00250-6
15. S, Yoshii K, Hara T, Yamashita K, Nakano K, Yamamoto N, et al. Deep Neural Networks for Dental Implant System Classification. Biomolecules.2020;10(7):984. https://doi.org/10.3390/biom10070984
16. Kim JE, Nam NE, Shim JS, Jung YH, Cho BH, Hwang JJ, et al. Transfer Learning via Deep Neural Networks for Implant Fixture System Classification Using Periapical Radiographs. J Clin Med. 2020;9(4):1117. https://doi.org/10.3390/jcm9041117
17. Kong HJ, Eom SH, Yoo JY, Lee JH. Identification of 130 dental implant types using ensemble deep learning. Int J Oral Maxillofac Implants. 2023;38(1):150–6. https://doi.org/10.11607/jomi.9818
18. Park W, Huh JK, Lee JH. Automated deep learning for classification of dental implant radiographs using a large multi-center dataset. Sci Rep. 2023;13(1):4862. https://doi.org/10.1038/s41598-023-32118-1
19. Mangano FG, Admakin O, Lerner H, Mangano C. Artificial intelligence and augmented reality for guided implant surgery planning: A proof of concept. J Dent. 2023;133:104485. https://doi.org/10.1016/j.jdent.2023.104485
20. Alsomali M, Alghamdi S, Alotaibi S, Alfadda S, Altwaijry N, Alturaiki I, et al. Development of a deep learning model for automatic localization of radiographic markers of proposed dental implant site locations. Saudi Dent J. 2022;34(3):220–5. https://doi.org/10.1016/j.sdentj.2022.01.002
21. Sakai T, Li H, Shimada T, Kita S, Iida M, Lee C, et al. Development of artificial intelligence model for supporting implant drilling protocol decision making. J Prosthodont Res. 2023;67(3):360–5. https://doi.org/10.2186/jpr.JPR_D_22_00053
22. Oliveira ALI, Baldisserotto C, Baldisserotto J. A Comparative Study on SVM and Constructive RBF Neural Network for Prediction of Success of Dental Implants. In: Sanfeliu A, Cortés ML, editors. Progress in Pattern Recognition, Image Analysis and Applications. CIARP . vol. 3773. Berlin, Heidelberg: Springer; 2005. https://doi.org/10.1007/11578079_104
23. Moayeri RS, Khalili M, Nazari M. A hybrid method to predict success of dental implants. Int J Adv Comput Sci Appl. 2016;7(5):1–6. https://doi.org/10.14569/IJACSA.2016.070501
24. Oh S, Kim YJ, Kim J, Jung JH, Lim HJ, Kim BC, et al. Deep learningbased prediction of osseointegration for dental implant using plain radiography. BMC Oral Health. 2023;23(1):208. https://doi.org/10.1186/s12903-023-02921-3
25. Ramachandran RA, Barão VVR, Ozevin D, Sukotjo C, Pai S, MathewM, et al. Early predicting tribocorrosion rate of dental implant titanium materials using random forest machine learning models. Tribol Int. 2023;187:108735. https://doi.org/10.1016/j.triboint.2023.108735.
26. Park J, Moon H, Jung H, Hwang J, Choi Y, Kim JE, et al. Deep learning and clustering approaches for dental implant size classification based on periapical radiographs. Sci Rep. 2023;13:16856. https://doi.org/10.1038/s41598-023-42385-7