Teeth and Technology: The Responsibility of Artificial Intelligence Techniques in the Dental Field- A Literature Review


  • Maad M. Mijwil Computer Techniques Engineering Department, Baghdad College of Economic Sciences University, Baghdad, Iraq https://orcid.org/0000-0002-2884-2504




Artificial intelligence, Databases, Dentistry, Explainability, Machine Learning, Medical field


With the significant growth of modern technology and its integration into many different industries, especially in the healthcare sector, artificial intelligence is one of the critical methods contributing to the development of medical fields, including dentistry. It possesses important and influential techniques that contribute to improving the results of patient care, diagnosis, treatment planning, and tracking the spread of diseases. These techniques play a major role in assisting dentists in diagnosing patients with high efficiency and accuracy. In this review, artificial intelligence techniques in developing the field of dentistry will be reviewed by highlighting the most important literature in which these techniques are involved. A search was conducted in Web of Science, Scopus, and PubMed databases from 2018 to 2023, where many articles were found (n=432), and articles that did not meet the selection criteria were excluded, resulting in thirty included. These articles involve artificial intelligence techniques in six areas: periodontal, dental implantology, forensic dentistry, oral medicine and pathology, orthodontics, and diagnostics/dentistry. In addition, this review presents matters related to artificial intelligence in dentistry, including data security, ethical concerns, and developing dentists' skills. This article finds that deep learning methods are widely utilized in the growth of dentistry, as the results show the accuracy of the results obtained, which is equivalent to the accuracy of professionals, and that it contributes to reducing human errors and revolutionizing the improvement of patient outcomes.


K. Somayaji, V. S. Muliya, M. R. KG, U. K. Malladi, and S. B. Nayak, “A literature review of the maxillary sinus with special emphasis on its anatomy and odontogenic diseases associated with it,” The Egyptian Journal of Otolaryngology, vol.39, no.173, pp.1-13, November 2023. https://doi.org/10.1186/s43163-023-00536-7

M. Labanca, M. Gianò, C. Franco, and R. Rezzani, “Orofacial Pain and Dentistry Management: Guidelines for a More Comprehensive Evidence-Based Approach,” Diagnostics, vol.13, no.17, pp.1-17, September 2023. https://doi.org/10.3390/diagnostics13172854

F. Angelone, A. M. Ponsiglione, C. Ricciardi, G. Cesarelli, M. Sansone, and F. Amato, “Diagnostic Applications of Intraoral Scanners: A Systematic Review,” Journal of Imaging, vol.9, no.7, pp.1-23, July 2023. https://doi.org/10.3390/jimaging9070134

C. Huang, J. Wang, S. Wang, and Y. Zhang, “A review of deep learning in dentistry,” Neurocomputing, vol.554, pp.126629, October 2023. https://doi.org/10.1016/j.neucom.2023.126629

M. Momeni-Moghaddam, C. Hashemi, A. Fathi, and F. Khamesipour, “Diagnostic accuracy, available treatment, and diagnostic methods of dental caries in practice: a meta-analysis,” Beni-Suef University Journal of Basic and Applied Sciences, vol.11, no.62, pp.1-11, May 2022. https://doi.org/10.1186/s43088-022-00243-x

S. Secinaro, D. Calandra, A. Secinaro, V. Muthurangu, and P. Biancone, “The role of artificial intelligence in healthcare: a structured literature review,” BMC Medical Informatics and Decision Making, vol.21, no.125, pp.1-23, April 2021. https://doi.org/10.1186/s12911-021-01488-9

D. Houfani, S. Slatnia, O. Kazar, H. Saouli, and A. Merizig, “Artificial intelligence in healthcare: a review on predicting clinical needs,” International Journal of Healthcare Management, vol.15, no.3, pp.267-275, February 2021. https://doi.org/10.1080/20479700.2021.1886478

A. Bohr and K. Memarzadeh, “The rise of artificial intelligence in healthcare applications,” Artificial Intelligence in Healthcare, pp. 25-60, 2020. https://doi.org/10.1016/B978-0-12-818438-7.00002-2

H. Hassani, E. S. Silva, S. Unger, M. TajMazinani, and S. M. Feely, “Artificial Intelligence (AI) or Intelligence Augmentation (IA): What Is the Future?,” AI, vol.1, no2., pp.143-155, April 2020. https://doi.org/10.3390/ai1020008

B. Shneiderman, “Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy,” International Journal of Human–Computer Interaction, vol.36, no.6, pp. 495-504, March 2020. https://doi.org/10.1080/10447318.2020.1741118

S. F. Wamba and M. M. Queiroz, “Responsible Artificial Intelligence as a Secret Ingredient for Digital Health: Bibliometric Analysis, Insights, and Research Directions,” Information Systems Frontiers, vol.25, pp.2123–2138, May 2021. https://doi.org/10.1007/s10796-021-10142-8

M. Sallam and D. Mousa, “Evaluating ChatGPT performance in Arabic dialects: A comparative study showing defects in responding to Jordanian and Tunisian general health prompts,” Mesopotamian Journal of Artificial Intelligence in Healthcare, vol.2024, pp .1–7, January 2024. https://doi.org/10.58496/MJAIH/2024/001

S. Pandya, A. Thakur, S. Saxena, N. Jassal, C. Patel, et al., “A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions,” Sensors, vol.21, no.23, pp.1-37, November 2021. https://doi.org/10.3390/s21237786

H. Liang, B. Y. Tsui, H. Ni, C. C. S. Valentim, S. L. Baxter, et al., “Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence,” Nature Medicine, vol.25, pp.433–438, February 2019. https://doi.org/10.1038/s41591-018-0335-9

M. Mirbabaie, S. Stieglitz, and N. R. J. Frick, “Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction,” Health and Technology, vol.11, pp. 693–731, May 2021. https://doi.org/10.1007/s12553-021-00555-5

F. Jiang, Y. Jiang, H. Zhi, Y. Dong, H. Li, et al., “Artificial intelligence in healthcare: past, present and future,” Stroke and Vascular Neurology, vol.2, pp.230-243, June 2017. https://doi.org/10.1136/svn-2017-000101

J. Amann, A. Blasimme, E. Vayena, D. Frey, and V. I. Madai, “Explainability for artificial intelligence in healthcare: a multidisciplinary perspective,” BMC Medical Informatics and Decision Making, vol.20, no.310, pp.1-9, November 2020. https://doi.org/10.1186/s12911-020-01332-6

B. Meskó, Z. Drobni, É. Bényei, B. Gergely, and Z. Győrffy, “Digital health is a cultural transformation of traditional healthcare,” Digital health is a cultural transformation of traditional healthcare, vol.3, no.9, pp.1-8, September 2017. http://dx.doi.org/10.21037/mhealth.201

S. K. Bhattamisra, P. Banerjee, P. Gupta, J. Mayuren, S. Patra, and M. Candasamy, “Artificial Intelligence in Pharmaceutical and Healthcare Research,” Big Data and Cognitive Computing, vol.7, no.1, pp.1-20, January 2023. https://doi.org/10.3390/bdcc7010010

H. I. W. Al-Shahwani and A. K. Faieq, “The Benefit of Artificial Intelligence in the Analysis of Malignant Brain Diseases: A Mini Review,” Mesopotamian Journal of Artificial Intelligence in Healthcare, vol.2023, pp.57–60, November 2023. https://doi.org/10.58496/MJAIH/2023/011

M. M. Mijwil, “Deep Convolutional Neural Network Architecture to Detection COVID-19 from Chest X-ray Images,” Iraqi Journal of Science, vol.64, no.5, pp:2561-2574, May 2023. https://doi.org/10.24996/ijs.2023.64.5.38.

M. Ghaderzadeh, M. Aria, and F. Asadi, “X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic,” BioMed Research International, vol.2021, no.9942873, pp.1-16, August 2021. https://doi.org/10.1155/2021/9942873

M. Alhasan and M. Hasaneen, “Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic,” Computerized Medical Imaging and Graphics, vol.91, pp.101933, July 2021. https://doi.org/10.1016/j.compmedimag.2021.101933

C. Mulrenan, K. Rhode, and B. M. Fischer, “A Literature Review on the Use of Artificial Intelligence for the Diagnosis of COVID-19 on CT and Chest X-ray,” Diagnostics, vol.12, no.4, pp.1-22, March 2022. https://doi.org/10.3390/diagnostics12040869

Z. Ahmed, W. Degroat, H. Abdelhalim, S. Zeeshan, and D. Fine, “Deciphering genomic signatures associating human dental oral craniofacial diseases with cardiovascular diseases using machine learning approaches,” Clinical Oral Investigations, vol.28, no.52, pp.1, January 2024. https://doi.org/10.1007/s00784-023-05406-3

N. Akkaya, G. Ünsal, and K. Orhan, “Understanding of AI in Dental Field with Technical Aspects,” In Artificial Intelligence in Dentistry, pp.9-31, January 2024. https://doi.org/10.1007/978-3-031-43827-1_2

J. R. v. Leeuwen, E. L. Penne, T. Rabelink, R. Knevel, and Y. K. O. Teng, “Using an artificial intelligence tool incorporating natural language processing to identify patients with a diagnosis of ANCA-associated vasculitis in electronic health records,” Computers in Biology and Medicine, vol.168, pp.107757, January 2024. https://doi.org/10.1016/j.compbiomed.2023.107757

V. G. V. Vydiswaran, A. Strayhorn, K. Weber, H. Stevens, J. Mellinger, et al., “Automated-detection of risky alcohol use prior to surgery using natural language processing,” Alcohol: Clinical and Experimental Research, vol.48, no.1, pp.153-163, January 2024. https://doi.org/10.1111/acer.15222

R. Bouali, O. Mahboub, and M. Lazaar, “Review of Dental Diagnosis by Deep Learning Models: Trends, Applications and Challenges,” Procedia Computer Science, vol.231, pp.221-228, 2024. https://doi.org/10.1016/j.procs.2023.12.196

H. Chen, P. Liu, Z. Chen, Q. Chen, Z. Wen, and Z. Xie, “Predicting sequenced dental treatment plans from electronic dental records using deep learning,” Artificial Intelligence in Medicine, vol.147, pp.102734, January 2024. https://doi.org/10.1016/j.artmed.2023.102734

F. Oztekin, O. Katar, F. Sadak, M. Yildirim, H. Cakar, et al., “An Explainable Deep Learning Model to Prediction Dental Caries Using Panoramic Radiograph Images,” Diagnostics, vol.13, no.2, pp.1-13, January 2023. https://doi.org/10.3390/diagnostics13020226

J. Kim, H. Lee, I. Song, and K. Jung, “DeNTNet: Deep Neural Transfer Network for the detection of periodontal bone loss using panoramic dental radiographs,” Scientific Reports, vol.9, no.17615, pp.1-9, November 2019. https://doi.org/10.1038/s41598-019-53758-2

J. Lee, D. Kim, S. Jeong, and S. Choi, “Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm,” Journal of Dentistry, vol.77, pp.106-111, October 2018. https://doi.org/10.1016/j.jdent.2018.07.015

S. Ha, H. S. Park, E. Kim, H. Kim, J. Yang, et al., “A pilot study using machine learning methods about factors influencing prognosis of dental implants,” The Journal of Advanced Prosthodontics, vol.10, no.6, pp.395-400, December 2018. https://doi.org/10.4047/jap.2018.10.6.395

R. Patcas, D. A. J. Bernini, A. Volokitin, E. Agustsson, R. Rothe, and R. Timofte, “Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age,” International Journal of Oral and Maxillofacial Surgery, vol.48, n.1, pp.77-83, January 2019. https://doi.org/10.1016/j.ijom.2018.07.010

D. V. Tuzoff, L. N. Tuzova, M. M. Bornstein, A. S. Krasnov, M. A. Kharchenko, et al., “Tooth detection and numbering in panoramic radiographs using convolutional neural networks,” Dentomaxillofacial Radiology, vol.48, no.4, pp.1-10, May 2019. https://doi.org/10.1259/dmfr.20180051

J. Krois, T. Ekert, L. Meinhold, T. Golla, B. Kharbot, et al., “Deep Learning for the Radiographic Detection of Periodontal Bone Loss,” Scientific Reports, vol. 9, no.8495, pp.1-6, June 2019. https://doi.org/10.1038/s41598-019-44839-3

H. Chang, S. Lee, T. Yong, N. Shin, B. Jang, et al., “Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis,” Scientific Reports, vol.10, no.7531, pp.1-8, May 2020. https://doi.org/10.1038/s41598-020-64509-z

Y. Yasa, Ö. Çelik, I. S. Bayrakdar, A. Pekince, K. Orhan, et al., “An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs,” Acta Odontologica Scandinavica, vol.79, no.4, pp.275-281, November 2020. https://doi.org/10.1080/00016357.2020.1840624

H. Yu, Z. Lin, Y. Liu, J. Su, B. Chen, and G. Lu, “A New Technique for Diagnosis of Dental Caries on the Children’s First Permanent Molar,” IEEE Access, vol.8, pp.185776 - 185785, October 2020. https://doi.org/10.1109/ACCESS.2020.3029454

T. Takahashi, K. Nozaki, T. Gonda, T. Mameno, M. Wada, K. Ikebe, “Identification of dental implants using deep learning—pilot study,” International Journal of Implant Dentistry, vol.6, no.53, pp.1-6, September 2020. https://doi.org/10.1186/s40729-020-00250-6

A. F. Leite, A. V. Gerven, H. Willems, T. Beznik, P. Lahoud, et al., “Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs,” Clinical Oral Investigations, vol.25, pp.2257–2267, August 2020. https://doi.org/10.1007/s00784-020-03544-6

M. Vranckx, A. V. Gerven, H. Willems, A. Vandemeulebroucke, A. F. Leite, et al., “Artificial Intelligence (AI)-Driven Molar Angulation Measurements to Predict Third Molar Eruption on Panoramic Radiographs,” International Journal of Environmental Research and Public Health, vol.17, no.10, pp.3716, May 2020. https://doi.org/10.3390/ijerph17103716

H. Kök, M. S. Izgi, and A. M. Acilar, “Determination of growth and development periods in orthodontics with artificial neural network,” Orthodontics & Craniofacial Research, vol.24, no.52, pp.76-83, November 2020. https://doi.org/10.1111/ocr.12443

D. Lee, S. Kim, S. Jeong, and J. Lee, “Artificial Intelligence in Fractured Dental Implant Detection and Classification: Evaluation Using Dataset from Two Dental Hospitals,” Diagnostics, vol.11, no.12, pp.233, February 2021. https://doi.org/10.3390/diagnostics11020233

S. K. Bayrakdar, K. Orhan, I. S. Bayrakdar, E. Bilgir, M. Ezhov, et al., “A deep learning approach for dental implant planning in cone-beam computed tomography images,” BMC Medical Imaging, vol.21, no.86, pp.1-9, May 2021. https://doi.org/10.1186/s12880-021-00618-z

Y. Ahn, J. J. Hwang, Y. Jung, T. Jeong, and J. Shin, “Automated Mesiodens Classification System Using Deep Learning on Panoramic Radiographs of Children,” Diagnostics, vo.11, no.8, pp.1477, August 2021. https://doi.org/10.3390/diagnostics11081477

H. Wang, J. Minnema, K. J. Batenburg, T. Forouzanfar, F. J. Hu, and G. Wu, “Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning,” Journal of Dental Research, vol.100, no.9, pp.943-949, August 2021. https://doi.org/10.1177/00220345211005338

M. C. Kılıc, I. S. Bayrakdar, Ö. Çelik, E. Bilgir, K. Orhan, et al., “Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs,” Dentomaxillofacial Radiology, vol.50, no.6, pp.1-7, March 2021. https://doi.org/10.1259/dmfr.20200172

E. Kaya, H. G. Gunec, S. S. Gokyay, S. Kutal, S. Gulum, and H. F. Ates, “Proposing a CNN Method for Primary and Permanent Tooth Detection and Enumeration on Pediatric Dental Radiographs,” Journal of Clinical Pediatric Dentistry, vol.46, no.4, pp.293–298, September 2022. https://doi.org/10.22514/1053-4625-46.4.6

B. Çelik and M. E. Çelik, “Automated detection of dental restorations using deep learning on panoramic radiographs,” Dentomaxillofacial Radiology, vol.51, no.8, pp.1-9, September 2022. https://doi.org/10.1259/dmfr.20220244

P. Engels, O. Meyer, J. Schönewolf, A. Schlickenrieder, R. Hickel, et al., “Automated detection of posterior restorations in permanent teeth using artificial intelligence on intraoral photographs,” Journal of Dentistry, vol.121, pp.104124, June 2022. https://doi.org/10.1016/j.jdent.2022.104124

S. Chen, T. Chen, Y. Huang, C. Chen, H. Chou, et al., “Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs,” IEEE Access, vol.10, pp.118654 - 118664, November 2022. https://doi.org/10.1109/ACCESS.2022.3220335

I. Kang, S. N. Njimbouom, K. Lee, and J. Kim, “DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine,” Applied Sciences, vol.12, no.6, pp.1-15, March 2022. https://doi.org/10.3390/app12063043

X. Zhou, G. Yu, Q. Yin, Y. Liu, Z. Zhang, and J. Sun, “Context Aware Convolutional Neural Network for Children Caries Diagnosis on Dental Panoramic Radiographs,” Computational and Mathematical Methods in Medicine, vol.2022, no.6029245, pp.1-8, September 2022. https://doi.org/10.1155/2022/6029245

B. Tiryaki, A. Ozdogan, M. T. Guller, O. Miloglu, E. A. Oral, and I. Y. Ozbek, “Dental implant brand and angle identification using deep neural networks,” The Journal of Prosthetic Dentistry, In Press, September 2023. https://doi.org/10.1016/j.prosdent.2023.07.022

J. Zhang, H. Lu, J. Hou, Q. Wang, F. Yu, et al., “Deep learning-based prediction of mandibular growth trend in children with anterior crossbite using cephalometric radiographs,” BMC Oral Health, vol.23, no.28, pp.1-8, January 2023. https://doi.org/10.1186/s12903-023-02734-4

M. Xu, Y. Wu, Z. Xu, P. Ding, H. Bai, and X. Deng, “Robust automated teeth identification from dental radiographs using deep learning,” Journal of Dentistry, vol.136, pp.104607, September 2023. https://doi.org/10.1016/j.jdent.2023.104607

G. Rubiu, M. Bologna, M. Cellina, M. Cè, D. Sala, et al., “Teeth Segmentation in Panoramic Dental X-ray Using Mask Regional Convolutional Neural Network,” Applied Sciences, vol.13, no.13, pp.1-14, July 2023. https://doi.org/10.3390/app13137947

J. Ryu, D. Lee, Y. Jung, O. Kwon, S. Park, et al., “Automated Detection of Periodontal Bone Loss Using Deep Learning and Panoramic Radiographs: A Convolutional Neural Network Approach,” Applied Sciences, vol.13, no.9, pp.1-10, April 2023. https://doi.org/10.3390/app13095261

I. D. S. Chen, C. Yang, M. Chen, M. Chen, R. Weng, and C. Yeh, “Deep Learning-Based Recognition of Periodontitis and Dental Caries in Dental X-ray Images,” Bioengineering, vol.10, no.8, pp.1-13, August 2023. https://doi.org/10.3390/bioengineering10080911

M. Abdaljaleel, M. Barakat, M. Alsanafi, N. A. Salim, H. Abazid, et al., “A multinational study on the factors influencing university students’ attitudes and usage of ChatGPT,” Scientific Reports, vol.14, no.1983, pp.1-14, January 2024. https://doi.org/10.1038/s41598-024-52549-8

E. Sadıkoğlu, M. Gök, M. M. Mijwil, and I. Kösesoy, “The Evolution and Impact of Chatbots in Social Media: Comprehensive Review of Past, Present, and Future Applications,” Veri Bilimi, vol.6, no.2, pp.67-76, December 2023.

M. Sallam, K. Al-Salahat, and Eyad Al-Ajlouni, “ChatGPT Performance in Diagnostic Clinical Microbiology Laboratory-Oriented Case Scenarios,” Cureus, vol.15, no.12, pp.1-11, December 2023. https://doi.org/10.7759/cureus.50629

J. G. Anderson and K. Abrahamson, “Your Health Care May Kill You: Medical Errors,” In Studies in Health Technology and Informatics, vol.234, pp.13-17, 2017. https://doi.org/10.3233/978-1-61499-742-9-13







How to Cite

M. M. Mijwil, “Teeth and Technology: The Responsibility of Artificial Intelligence Techniques in the Dental Field- A Literature Review”, WJCMS, vol. 3, no. 1, pp. 1–17, Mar. 2024, doi: 10.31185/wjcms.240.

Most read articles by the same author(s)