OBJECTIVES: Artificial Intelligence (AI), particularly deep learning, has significantly impacted healthcare, including dentistry, by improving diagnostics, treatment planning, and prognosis prediction. This systematic mapping review explores the current applications of deep learning in dentistry, offering a comprehensive overview of trends, models, and their clinical significance. MATERIALS AND METHODS: Following a structured methodology, relevant studies published from January 2012 to September 2023 were identified through database searches in PubMed, Scopus, and Embase. Key data, including clinical purpose, deep learning tasks, model architectures, and data modalities, were extracted for qualitative synthesis. RESULTS: From 21,242 screened studies, 1,007 were included. Of these, 63.5% targeted diagnostic tasks, primarily with convolutional neural networks (CNNs). Classification (43.7%) and segmentation (22.9%) were the main methods, and imaging data-such as cone-beam computed tomography and orthopantomograms-were used in 84.4% of cases. Most studies (95.2%) applied fully supervised learning, emphasizing the need for annotated data. Pathology (21.5%), radiology (17.5%), and orthodontics (10.2%) were prominent fields, with 24.9% of studies relating to more than one specialty. CONCLUSION: This review explores the advancements in deep learning in dentistry, particulary for diagnostics, and identifies areas for further improvement. While CNNs have been used successfully, it is essential to explore emerging model architectures, learning approaches, and ways to obtain diverse and reliable data. Furthermore, fostering trust among all stakeholders by advancing explainable AI and addressing ethical considerations is crucial for transitioning AI from research to clinical practice. CLINICAL RELEVANCE: This review offers a comprehensive overview of a decade of deep learning in dentistry, showcasing its significant growth in recent years. By mapping its key applications and identifying research trends, it provides a valuable guide for future studies and highlights emerging opportunities for advancing AI-driven dental care.

College of Dentistry University of Saskatchewan Saskatoon SK Canada

Dentofacial Deformities Research Center Research Institute of Dental Sciences Shahid Beheshti University of Medical Sciences Tehran Iran

Department of Conservative Dentistry and Periodontology LMU University Hospital LMU Munich Munich Germany

Department of Dentistry and Oral Health Aarhus University Vennelyst Boulevard 9 Aarhus C 8000 Aarhus Denmark

Department of Orthodontics School of Dentistry Shahid Beheshti University of Medical Sciences Tehran Iran

Division of Orthodontics The Ohio State University Columbus OH 43210 USA

Institute of Dental Medicine 1st Faculty of Medicine of the Charles University and General University Hospital Prague Czech Republic

ITU WHO WIPO Global Initiative on Artificial Intelligence for Health Dental Diagnostics and Digital Dentistry Geneva Switzerland

Medical Physics and Biomedical Engineering Department School of Medicine Tehran University of Medical Sciences Tehran Iran

Research Center for Biomedical Technologies and Robotics Tehran Iran

Zobrazit více v PubMed

Li Y-H, Li Y-L, Wei M-Y, Li G-Y (2024) Innovation and challenges of artificial intelligence technology in personalized healthcare. Sci Rep 14:18994. https://doi.org/10.1038/s41598-024-70073-7 PubMed DOI PMC

McCarthy J, Minsky ML, Rochester N, Shannon CE (2006) A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI Mag 27:12–12. https://doi.org/10.1609/aimag.v27i4.1904 DOI

Pirayesh Z, Hassanzadeh-Samani S, Farzan A, Rohban MH, Ghorbanimehr MS, Mohammad-Rahimi H, Motamedian SR (2023) A deep learning framework to scale linear facial measurements to actual size using horizontal visible iris diameter: a study on an Iranian population. Sci Rep 13:13755. https://doi.org/10.1038/s41598-023-40839-6 PubMed DOI PMC

Motamedian SR, Hassanzadeh-Samani S, Nadimi M, Shobeiri P, Motie P, Rohban MH, Mahmoudinia E and Mohammad-Rahimi H (2023) Brief Introduction to Artificial Intelligence and Machine Learning. In: Khojasteh A, Ayoub AF and Nadjmi N (eds), Emerging Technologies in Oral and Maxillofacial Surgery (pp. 267–285). Springer Nature Singapore, Singapore. https://doi.org/10.1007/978-981-19-8602-4_14

Lee J-H, Kim D-H, Jeong S-N, Choi S-H (2018) Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent 77:106–111. https://doi.org/10.1016/j.jdent.2018.07.015 PubMed DOI

Lim H-K, Jung S-K, Kim S-H, Cho Y, Song I-S (2021) Deep semi-supervised learning for automatic segmentation of inferior alveolar nerve using a convolutional neural network. BMC Oral Health 21:1–9. https://doi.org/10.1186/s12903-021-01983-5 DOI

Veeraraghavan VP, Daniel S, Dasari AK, Aileni KR, Patil C, Patil SR (2024) Harnessing artificial intelligence for predictive modelling in oral oncology: Opportunities, challenges, and clinical Perspectives. Oral Oncol Rep 11:100591. https://doi.org/10.1016/j.oor.2024.100591 DOI

Welikala RA, Remagnino P, Lim JH, Chan CS, Rajendran S, Kallarakkal TG, Zain RB, Jayasinghe RD, Rimal J, Kerr AR (2020) Automated detection and classification of oral lesions using deep learning for early detection of oral cancer. IEEE Access 8:132677–132693. https://doi.org/10.3390/cancers13112766 DOI

Mohammad-Rahimi H, Sohrabniya F, Ourang SA, Dianat O, Aminoshariae A, Nagendrababu V, Dummer PMH, Duncan HF, Nosrat A (2024) Artificial intelligence in endodontics: Data preparation, clinical applications, ethical considerations, limitations, and future directions. IEJ 57:1566–1595. https://doi.org/10.1111/iej.14128 DOI

Ourang SA, Sohrabniya F, Mohammad-Rahimi H, Dianat O, Aminoshariae A, Nagendrababu V, Dummer PMH, Duncan HF, Nosrat A (2024) Artificial intelligence in endodontics: Fundamental principles, workflow, and tasks. IEJ 57:1546–1565. https://doi.org/10.1111/iej.14127 DOI

Lee J-H, Kim D-h, Jeong S-N, Choi S-H (2018) Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci 48:114–123. https://doi.org/10.5051/jpis.2018.48.2.114 PubMed DOI PMC

Mohammad-Rahimi H, Motamadian SR, Nadimi M, Hassanzadeh-Samani S, Minabi MA, Mahmoudinia E, Lee VY, Rohban MH (2022) Deep learning for the classification of cervical maturation degree and pubertal growth spurts: A pilot study. Korean J Orthod 52:112–122. https://doi.org/10.4041/kjod.2022.52.2.112 PubMed DOI PMC

Zhou L, Pan S, Wang J, Vasilakos AV (2017) Machine learning on big data: Opportunities and challenges. Neurocomputing 237:350–361. https://doi.org/10.1016/j.neucom.2017.01.026 DOI

Sarker IH (2021) Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput Sci 2:420. https://doi.org/10.1007/s42979-021-00815-1 PubMed DOI PMC

Brahmi W, Jdey I, Drira F (2024) Exploring the role of convolutional neural networks (CNN) in dental radiography segmentation: A comprehensive systematic literature review. Eng Appl Artif Intell 133:108510. https://doi.org/10.1016/j.engappai.2024.108510 DOI

Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958

Konda K, Bouthillier X, Memisevic R, Vincent P (2015) Dropout as data augmentation stat 1050:29. https://doi.org/10.48550/arXiv.1506.08700

Thirumuruganathan S, Tang N, Ouzzani M, Doan AH (2020) Data curation with deep learning. In: Bonifati A, Zhou Y, Vaz Salles MA, Bohm A, Olteanu D, Fletcher G, Khan A, Yang B (eds) Advances in Database Technology - EDBT 2020: 23rd International Conference on Extending Database Technology, Proceedings (pp 277–286). (Advances in Database Technology - EDBT; vol 2020-March). OpenProceedings.org. https://doi.org/10.5441/002/edbt.2020.25

Willemink MJ, Koszek WA, Hardell C, Wu J, Fleischmann D, Harvey H, Folio LR, Summers RM, Rubin DL, Lungren MP (2020) Preparing medical imaging data for machine learning. Radiology 295:4–15. https://doi.org/10.1148/radiol.2020192224 PubMed DOI

Kazeminia S, Baur C, Kuijper A, van Ginneken B, Navab N, Albarqouni S, Mukhopadhyay A (2020) GANs for medical image analysis. Artif Intell Med 109:101938. https://doi.org/10.1016/j.artmed.2020.101938 PubMed DOI

Jaton F (2021) Assessing biases, relaxing moralism: On ground-truthing practices in machine learning design and application. Big Data Soc 8:20539517211013570. https://doi.org/10.1177/20539517211013569 DOI

Antoniadi AM, Du Y, Guendouz Y, Wei L, Mazo C, Becker BA, Mooney C (2021) Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review. Appl Sci 11:5088. https://doi.org/10.3390/app11115088 DOI

Goodfellow I, Bengio I, Courville A (2016) Deep learning (adaptive computation and machine learning series). The MIT Press, Cambridge

Kruse R, Mostaghim S, Borgelt C, Braune C and Steinbrecher M (2022) Multi-layer perceptrons. Computational Intelligence: A Methodological Introduction. Springer.  https://doi.org/10.1007/978-3-030-42227-1_5

Smart P, Thanammal K and Sujatha S (2023) An Ontology Based Multilayer Perceptron for Object Detection. Comput Syst Sci Eng 44. https://doi.org/10.32604/csse.2023.028053

Kufel J, Bargieł-Łączek K, Kocot S, Koźlik M, Bartnikowska W, Janik M, Czogalik Ł, Dudek P, Magiera M, Lis A (2023) What is machine learning, artificial neural networks and deep learning?—Examples of practical applications in medicine. Diagnostics 13:2582. https://doi.org/10.3390/diagnostics13152582 PubMed DOI PMC

Cherian JM, Kumar R (2023) Fundamentals of machine learning. A guide to applied machine learning for biologists. Cham: Springer Inter Pub, pp 147–174.  https://doi.org/10.1007/978-3-031-22206-1_6

Iman M, Arabnia HR, Rasheed K (2023) A review of deep transfer learning and recent advancements. Technologies 11:40. https://doi.org/10.3390/technologies11020040 DOI

Krishna ST, Kalluri HK (2019) Deep learning and transfer learning approaches for image classification. Int J Recent Technol Eng 7:427–432

Yilmaz S, Tasyurek M, Amuk M, Celik M, Canger EM (2024) Developing deep learning methods for classification of teeth in dental panoramic radiography. Oral Surg Oral Med Oral Pathol Oral Radiol 138:118–127. https://doi.org/10.1016/j.oooo.2023.02.021 PubMed DOI

Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A, Fujita H, Ariji E (2019) A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofac Radiol 48:20180218. https://doi.org/10.1259/dmfr.20180218 PubMed DOI

Ragodos R, Wang T, Padilla C, Hecht JT, Poletta FA, Orioli IM, Buxó CJ, Butali A, Valencia-Ramirez C, Restrepo Muñeton C et al (2022) Dental anomaly detection using intraoral photos via deep learning. Sci Rep 12:11577.  https://doi.org/10.1038/s41598-022-15788-1

Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60:84–90. https://doi.org/10.1145/3065386 DOI

Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63:139–144. https://doi.org/10.1145/3422622 DOI

Jain KR, Chauhan N, Jain KR and Chauhan N (2019) Clustering techniques for dental image analysis. Dental Image Analysis for Disease Diagnosis:103–128. https://doi.org/10.1007/978-3-030-14136-3_6

Park J-H, Moon HS, Jung H-I, Hwang J, Choi Y-H, Kim J-E (2023) Deep learning and clustering approaches for dental implant size classification based on periapical radiographs. Sci Rep 13:16856. https://doi.org/10.1038/s41598-023-42385-7 PubMed DOI PMC

Zhang P, Kamel Boulos MN (2023) Generative AI in medicine and healthcare: promises, opportunities and challenges. Future Internet 15:286. https://doi.org/10.3390/fi15090286 DOI

Umer F, Adnan N (2024) Generative artificial intelligence: synthetic datasets in dentistry. BDJ Open 10:13. https://doi.org/10.1038/s41405-024-00198-4 PubMed DOI PMC

Harms J, Lei Y, Wang T, Zhang R, Zhou J, Tang X, Curran WJ, Liu T, Yang X (2019) Paired cycle-GAN-based image correction for quantitative cone-beam computed tomography. Med Phys 46:3998–4009. https://doi.org/10.1002/mp.13656 PubMed DOI PMC

Zhan Y, Dewan M, Harder M, Krishnan A, Zhou XS (2011) Robust automatic knee MR slice positioning through redundant and hierarchical anatomy detection. IEEE Trans Med Imaging 30:2087–2100. https://doi.org/10.1109/TMI.2011.2162634 PubMed DOI

Schwing AG and Zheng Y (2014) Reliable extraction of the mid-sagittal plane in 3D brain MRI via hierarchical landmark detection. IEEE 11th International Symposium on Biomedical Imaging (ISBI) (pp. 213–216). IEEE. https://doi.org/10.1109/isbi.2014.6867847

Zheng Y, Liu D, Georgescu B, Nguyen H and Comaniciu D (2015) 3D deep learning for efficient and robust landmark detection in volumetric data. Medical Image Computing and Computer-Assisted Intervention. Springer, 2015. https://doi.org/10.1007/978-3-319-24553-9_69

Huang Y, Fan F, Syben C, Roser P, Mill L, Maier A (2021) Cephalogram synthesis and landmark detection in dental cone-beam CT systems. Med Image Anal 70:102028. https://doi.org/10.1016/j.media.2021.102028 PubMed DOI

Dai J, Guo X, Zhang H, Xie H, Huang J, Huang Q, Huang B (2024) Cone-beam CT landmark detection for measuring basal bone width: a retrospective validation study. BMC Oral Health 24:1091. https://doi.org/10.1186/s12903-024-04798-2 PubMed DOI PMC

Lee J-H, Yu H-J, Kim M-j, Kim J-W, Choi J (2020) Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks. BMC Oral Health 20:1–10. https://doi.org/10.1186/s12903-020-01256-7 DOI

Cheng M, Zhang X, Wang J, Yang Y, Li M, Zhao H, Huang J, Zhang C, Qian D, Yu H (2023) Prediction of orthognathic surgery plan from 3D cephalometric analysis via deep learning. BMC Oral Health 23:161. https://doi.org/10.1186/s12903-023-02844-z PubMed DOI PMC

Arsiwala-Scheppach LT, Chaurasia A, Müller A, Krois J, Schwendicke F (2023) Machine learning in dentistry: a scoping review. J Clin Med 12:937. https://doi.org/10.3390/jcm12030937 PubMed DOI PMC

Soltani P, Sohrabniya F, Mohammad-Rahimi H, Mehdizadeh M, Mohammadreza Mousavi S, Moaddabi A, Mohammadmahdi Mousavi S, Spagnuolo G, Yavari A, Schwendicke F (2024) A two-stage deep-learning model for determination of the contact of mandibular third molars with the mandibular canal on panoramic radiographs. BMC Oral Health 24:1373. https://doi.org/10.1186/s12903-024-04850-1 PubMed DOI PMC

Yüksel AE, Gültekin S, Simsar E, Özdemir ŞD, Gündoğar M, Tokgöz SB, Hamamcı İE (2021) Dental enumeration and multiple treatment detection on panoramic X-rays using deep learning. Sci Rep 11:12342. https://doi.org/10.1038/s41598-021-90386-1 PubMed DOI PMC

Han X-F, Laga H, Bennamoun M (2019) Image-based 3D object reconstruction: State-of-the-art and trends in the deep learning era. IEEE Trans Pattern Anal Mach Intell 43:1578–1604. https://doi.org/10.1109/TPAMI.2019.2954885 DOI

Cen Y, Huang X, Liu J, Qin Y, Wu X, Ye S, Du S, Liao W (2023) Application of three-dimensional reconstruction technology in dentistry: a narrative review. BMC Oral Health 23:1–21. https://doi.org/10.1186/s12903-023-03142-4 DOI

Qian J, Lu S, Gao Y, Tao Y, Lin J, Lin H (2021) An automatic tooth reconstruction method based on multimodal data. J Vis 24:205–221. https://doi.org/10.1007/s12650-020-00697-0 DOI

Liang K, Zhang L, Yang H, Yang Y, Chen Z, Xing Y (2019) Metal artifact reduction for practical dental computed tomography by improving interpolation-based reconstruction with deep learning. Med Phys 46:e823–e834. https://doi.org/10.1002/mp.13644 PubMed DOI

Guan S-Y, Wang T-M, Meng C, Wang J-C (2018) A review of point feature based medical image registration. Chin J Mech Eng 31:1–16. https://doi.org/10.1186/s10033-018-0275-9 DOI

Chen H, Qu Z, Tian Y, Jiang N, Qin Y, Gao J, Zhang R, Ma Y, Jin Z, Zhai G (2024) A cross-temporal multimodal fusion system based on deep learning for orthodontic monitoring. Comput Biol Med 180:109025. https://doi.org/10.1016/j.compbiomed.2024.109025 PubMed DOI

Haskins G, Kruger U, Yan P (2020) Deep learning in medical image registration: a survey. Mach Vis Appl 31:8. https://doi.org/10.1007/s00138-020-01060-x DOI

Abdi AH, Hannam AG, Fels S (2018) Fiducial-based fusion of 3D dental models with magnetic resonance imaging. Int J Comput Assist Radiol Surg 13:1109–1115. https://doi.org/10.1007/s11548-018-1767-x PubMed DOI

Srimaneekarn N, Hayter A, Liu W and Tantipoj C (2022) Binary response analysis using logistic regression in dentistry. Int J Dent 2022. https://doi.org/10.1155/2022/5358602

Rokhshad R, Mohammad-Rahimi H, Sohrabniya F, Jafari B, Shobeiri P, Tsolakis IA, Ourang SA, Sultan AS, Khawaja SN, Bavarian R, Palomo JM (2024) Deep learning for temporomandibular joint arthropathies: A systematic review and meta-analysis. J Oral Rehabil 51:1632–1644. https://doi.org/10.1111/joor.13701 PubMed DOI

Chau AMH, Lo ECM, Wong MCM, Chu CH (2018) Interpreting poisson regression models in dental caries studies. Caries Res 52:339–345. https://doi.org/10.1159/000486970 PubMed DOI

Song I-S, Shin H-K, Kang J-H, Kim J-E, Huh K-H, Yi W-J, Lee S-S, Heo M-S (2022) Deep learning-based apical lesion segmentation from panoramic radiographs. Imaging Sci Dent 52:351 PubMed DOI PMC

Choi H, Jeon KJ, Kim YH, Ha E-G, Lee C, Han S-S (2022) Deep learning-based fully automatic segmentation of the maxillary sinus on cone-beam computed tomographic images. Sci Rep 12:14009. https://doi.org/10.5624/isd.20220078 PubMed DOI PMC

Cunningham P, Cord M, Delany SJ (2008) Supervised learning. Springer, Machine Learning Techniques for Multimedia. https://doi.org/10.1007/978-3-540-75171-7_2 DOI

Krishnan R, Rajpurkar P, Topol EJ (2022) Self-supervised learning in medicine and healthcare. Nat Biomed Eng 6:1346–1352. https://doi.org/10.1038/s41551-022-00914-1 PubMed DOI

Hady MFA and Schwenker F (2013) Semi-supervised learning. Handbook Neural Infor Proc 215–239. https://doi.org/10.1007/978-3-642-36657-4_7

Usama M, Qadir J, Raza A, Arif H, Yau K-LA, Elkhatib Y, Hussain A, Al-Fuqaha A (2019) Unsupervised machine learning for networking: Techniques, applications and research challenges. IEEE Access 7:65579–65615. https://doi.org/10.1109/ACCESS.2019.2916648 DOI

James KL, Randall NP, Haddaway NR (2016) A methodology for systematic mapping in environmental sciences. Environ Evid 5:7. https://doi.org/10.1186/s13750-016-0059-6 DOI

Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25:44–56. https://doi.org/10.1038/s41591-018-0300-7 PubMed DOI

Allani H, Santos AT, Ribeiro-Vidal H (2024) Multidisciplinary applications of AI in dentistry: Bibliometric review. Appl Sci 2076–3417:14. https://doi.org/10.3390/app14177624 DOI

Lu W, Yu X, Li Y, Cao Y, Chen Y, Hua F (2024) Artificial intelligence-related dental research: Bibliometric and altmetric analysis. Int Dent J. https://doi.org/10.1016/j.identj.2024.08.004 PubMed DOI PMC

Karpov OE, Pitsik EN, Kurkin SA, Maksimenko VA, Gusev AV, Shusharina NN and Hramov AE (2023) Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach. Int J Environ Res Public Health 20. https://doi.org/10.3390/ijerph20075335

Mohammad-Rahimi H, Motamedian SR, Rohban MH, Krois J, Uribe SE, Mahmoudinia E, Rokhshad R, Nadimi M, Schwendicke F (2022) Deep learning for caries detection: A systematic review. J Dent 122:104115. https://doi.org/10.1016/j.jdent.2022.104115 PubMed DOI

Katsumata A (2023) Deep learning and artificial intelligence in dental diagnostic imaging. Jpn Dent Sci Rev 59:329–333. https://doi.org/10.1016/j.jdsr.2023.09.004 PubMed DOI PMC

Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X and Liao L (2022) Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Frontiers in Oncology 12. https://doi.org/10.3389/fonc.2022.773840

Hwang JJ, Jung YH, Cho BH, Heo MS (2019) An overview of deep learning in the field of dentistry. Imaging Sci Dent 49:1–7. https://doi.org/10.5624/isd.2019.49.1.1 PubMed DOI PMC

Schwendicke F, Golla T, Dreher M, Krois J (2019) Convolutional neural networks for dental image diagnostics: A scoping review. J Dent 91:103226. https://doi.org/10.1016/j.jdent.2019.103226 PubMed DOI

Khan S, Naseer M, Hayat M, Zamir SW, Khan FS, Shah M (2022) Transformers in vision: A survey. ACM computing surveys (CSUR) 54:1–41. https://doi.org/10.1145/3505244 DOI

Dosovitskiy A (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929. https://doi.org/10.48550/arXiv.2010.11929

Koohi-Moghadam M, Bae KT (2023) Generative AI in medical imaging: applications, challenges, and ethics. J Med Syst 47:94. https://doi.org/10.1007/s10916-023-01987-4 PubMed DOI

Salehi AW, Khan S, Gupta G, Alabduallah BI, Almjally A, Alsolai H, Siddiqui T, Mellit A (2023) A study of CNN and transfer learning in medical imaging: Advantages, challenges, future scope. Sustainability 15:5930. https://doi.org/10.3390/su15075930 DOI

Raghu M, Zhang C, Kleinberg J and Bengio S (2019) Transfusion: understanding transfer learning for medical imaging. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), pp. 3347–3357. Curran Associates Inc. https://doi.org/10.48550/arXiv.1902.07208

Lebovitz S, Levina N, Lifshitz-Assaf H (2021) Is AI ground truth really true? The dangers of training and evaluating AI tools based on experts’ know-what. MIS Q 45:1501–1526. https://doi.org/10.25300/MISQ/2021/16564 DOI

Venkatesh E, Elluru SV (2017) Cone beam computed tomography: basics and applications in dentistry. J Istanb Univ Fac Dent 51:102–121. https://doi.org/10.17096/jiufd.00289 DOI

Tichý A, Kunt L, Nagyová V, Kybic J (2024) Automatic caries detection in bitewing radiographs—Part II: experimental comparison. Clin Oral Invest 28:133. https://doi.org/10.1007/s00784-024-05528-2 DOI

Raza K, Singh NK (2021) A tour of unsupervised deep learning for medical image analysis. Curr Med Imaging 17:1059–1077. https://doi.org/10.2174/1573405617666210127154257 PubMed DOI

Shafi I, Fatima A, Afzal H, IdlT D, Lipari V, Breñosa J, Ashraf I (2023) A comprehensive review of recent advances in artificial intelligence for dentistry E-health. Diagnostics 13:2196. https://doi.org/10.3390/diagnostics13132196 PubMed DOI PMC

Lee C, Kumar S, Vogt KA and Meraj S (2024) Improving clinical documentation with AI: A comparative study of sporo AI scribe and GPT-4o mini. arXiv preprint arXiv:241015528. https://doi.org/10.48550/arXiv.2410.15528

Mohammad-Rahimi H, Ourang SA, Pourhoseingholi MA, Dianat O, Dummer PMH, Nosrat A (2024) Validity and reliability of artificial intelligence chatbots as public sources of information on endodontics. IEJ 57:305–314. https://doi.org/10.1111/iej.14014 DOI

Umer F, Batool I, Naved N (2024) Innovation and application of large language models (LLMs) in dentistry–a scoping review. BDJ Open 10:90. https://doi.org/10.1038/s41405-024-00277-6 PubMed DOI PMC

Safranek CW, Sidamon-Eristoff AE, Gilson A, Chartash D (2023) The role of large language models in medical education: applications and implications. Acad Med JMIR Pub Toronto, Canada 98(3):345–350. https://doi.org/10.2196/50945 DOI

Feher B, Tussie C, Giannobile WV (2024) Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches. Front Artif Intell 7:1427517. https://doi.org/10.3389/frai.2024.1427517 PubMed DOI PMC

Paraschiv E-A, Cîrnu CE, Vevera AV (2024) Integrating artificial intelligence and cybersecurity in electronic health records: Addressing challenges and optimizing healthcare systems. https://doi.org/10.5772/intechopen.1007041

Zhou X, Li Y, Liang W (2020) CNN-RNN based intelligent recommendation for online medical pre-diagnosis support. IEEE/ACM Trans Comput Biol Bioinform 18:912–921. https://doi.org/10.1109/TCBB.2020.2994780 DOI

Walsh L, Brostek A (2013) Minimum intervention dentistry principles and objectives. Aust Dent J 58:3–16. https://doi.org/10.1111/adj.12045 PubMed DOI

Mahesh Batra A, Reche A (2023) A new era of dental care: Harnessing artificial intelligence for better diagnosis and treatment. Cureus 15:e49319. https://doi.org/10.7759/cureus.49319 PubMed DOI PMC

Lian L, Zhu T, Zhu F, Zhu H (2021) Deep learning for caries detection and classification. Diagnostics 11:1672. https://doi.org/10.3390/diagnostics11091672 PubMed DOI PMC

López-Cortés XA, Matamala F, Venegas B, Rivera C (2022) Machine-learning applications in oral cancer: A systematic review. Appl Sci 12:5715. https://doi.org/10.3390/app12115715 DOI

Warin K, Suebnukarn S (2024) Deep learning in oral cancer- a systematic review. BMC Oral Health 24:212. https://doi.org/10.1186/s12903-024-03993-5 PubMed DOI PMC

Abdul NS, Shivakumar GC, Sangappa SB, Di Blasio M, Crimi S, Cicciù M, Minervini G (2024) Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta-analysis. BMC Oral Health 24:122. https://doi.org/10.1186/s12903-023-03533-7 PubMed DOI PMC

Kim DW, Lee S, Kwon S, Nam W, Cha I-H, Kim HJ (2019) Deep learning-based survival prediction of oral cancer patients. Sci Rep 9:6994. https://doi.org/10.1038/s41598-019-43372-7 PubMed DOI PMC

Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N (2021) Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 13:1–17. https://doi.org/10.1186/s13073-021-00968-x DOI

Chang H-J, Lee S-J, Yong T-H, Shin N-Y, Jang B-G, Kim J-E, Huh K-H, Lee S-S, Heo M-S, Choi S-C (2020) Deep learning hybrid method to automatically diagnose periodontal bone loss and stage periodontitis. Sci Rep 10:7531. https://doi.org/10.1038/s41598-020-64509-z PubMed DOI PMC

Liu Q, Dai F, Zhu H, Yang H, Huang Y, Jiang L, Tang X, Deng L, Song L (2023) Deep learning for the early identification of periodontitis: a retrospective, multicentre study. Clin Radiol 78:e985–e992. https://doi.org/10.1016/j.crad.2023.08.017 PubMed DOI

Ben-Israel D, Jacobs WB, Casha S, Lang S, Ryu WHA, de Lotbiniere-Bassett M, Cadotte DW (2020) The impact of machine learning on patient care: A systematic review. Artif Intell Med 103:101785. https://doi.org/10.1016/j.artmed.2019.101785 PubMed DOI

Batra P, Tagra H and Katyal S (2022) Artificial intelligence in teledentistry. Discoveries 10. https://doi.org/10.15190/d.2022.12

Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J (2019) A guide to deep learning in healthcare. Nat Med 25:24–29. https://doi.org/10.1038/s41591-018-0316-z PubMed DOI

Larrazabal AJ, Nieto N, Peterson V, Milone DH, Ferrante E (2020) Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proc Natl Acad Sci 117:12592–12594. https://doi.org/10.1073/pnas.1919012117 PubMed DOI PMC

Noseworthy PA, Attia ZI, Brewer LC, Hayes SN, Yao X, Kapa S, Friedman PA, Lopez-Jimenez F (2020) Assessing and mitigating bias in medical artificial intelligence: the effects of race and ethnicity on a deep learning model for ECG analysis. Circ Arrhythm Electrophysiol 13:e007988. https://doi.org/10.1161/CIRCEP.119.007988 PubMed DOI PMC

Rai A (2020) Explainable AI: From black box to glass box. J Acad Mark Sci 48:137–141. https://doi.org/10.1007/s11747-019-00710-5 DOI

Von Eschenbach WJ (2021) Transparency and the black box problem: Why we do not trust AI. Philos Technol 34:1607–1622. https://doi.org/10.1007/s13347-021-00477-0 DOI

Tursunalieva A, Alexander DL, Dunne R, Li J, Riera L, Zhao Y (2024) Making sense of machine learning: A review of interpretation techniques and their applications. Appl Sci 14:496. https://doi.org/10.3390/app14020496 DOI

Ducret M, Wahal E, Gruson D, Amrani S, Richert R, Mouncif-Moungache M, Schwendicke F (2024) Trustworthy artificial intelligence in dentistry: Learnings from the EU AI Act. J Dent Res 103:1051–1056. https://doi.org/10.1177/00220345241271160 PubMed DOI PMC

Gu X, Sabrina F, Fan Z, Sohail S (2023) A review of privacy enhancement methods for federated learning in healthcare systems. Int J Environ Res Public Health 20:6539. https://doi.org/10.3390/ijerph20156539 PubMed DOI PMC

Ferrara E (2023) (2023) Fairness and bias in artificial intelligence: A brief survey of sources, impacts, and mitigation strategies. IEEE Trans Knowl Data Eng 35(1):1–14. https://doi.org/10.3390/sci6010003 DOI

Uribe SE, Issa J, Sohrabniya F, Denny A, Kim NN, Dayo AF, Chaurasia A, Sofi-Mahmudi A, Büttner M and Schwendicke F (2024) Publicly available dental image datasets for artificial intelligence. J Dent Res:00220345241272052. https://doi.org/10.1177/00220345241272052

Seyyed-Kalantari L, Zhang H, McDermott MB, Chen IY, Ghassemi M (2021) Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat Med 27:2176–2182. https://doi.org/10.1038/s41591-021-01595-0 PubMed DOI PMC

Armstrong N (2018) Overdiagnosis and overtreatment as a quality problem: insights from healthcare improvement research. BMJ Qual Saf 27:571–575. https://doi.org/10.1136/bmjqs-2017-007571 PubMed DOI

Schwendicke F, Samek W, Krois J (2020) Artificial intelligence in dentistry: Chances and challenges. J Dent Res 99:769–774. https://doi.org/10.1177/0022034520915714 PubMed DOI

Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ (2020) Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med 26:1351–1363. https://doi.org/10.1038/s41591-020-1037-7 PubMed DOI PMC

Schwendicke F, Singh T, Lee J-H, Gaudin R, Chaurasia A, Wiegand T, Uribe S, Krois J (2021) Artificial intelligence in dental research: Checklist for authors, reviewers, readers. J Dent 107:103610. https://doi.org/10.1016/j.jdent.2021.103610 DOI

Chen YW, Stanley K, Att W (2020) Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int 51:248–257. https://doi.org/10.3290/j.qi.a43952 PubMed DOI

Rokhshad R, Keyhan SO, Yousefi P (2023) Artificial intelligence applications and ethical challenges in oral and maxillo-facial cosmetic surgery: a narrative review. Maxillofac Plast Reconstr Surg 45:14. https://doi.org/10.1186/s40902-023-00382-w PubMed DOI PMC

Naik N, Hameed BZ, Shetty DK, Swain D, Shah M, Paul R, Aggarwal K, Ibrahim S, Patil V, Smriti K (2022) Legal and ethical consideration in artificial intelligence in healthcare: Who takes responsibility? Front Surg 9:862322. https://doi.org/10.3389/fsurg.2022.862322 PubMed DOI PMC

Lipkova J, Chen RJ, Chen B, Lu MY, Barbieri M, Shao D, Vaidya AJ, Chen C, Zhuang L, Williamson DF (2022) Artificial intelligence for multimodal data integration in oncology. Cancer Cell 40:1095–1110. https://doi.org/10.1016/j.ccell.2022.09.012 PubMed DOI PMC