The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry
Open accessAbstract
Artificial intelligence (AI) holds immense promise in revolutionising dentistry, spanning, diagnostics, treatment planning and educational realms. This narrative review, in two parts, explores the fundamentals and the multifaceted potential of AI in dentistry. The current article explores the profound impact of AI in dentistry, encompassing diagnostic tools, treatment planning, and patient care. The Part 2 of the article delves into the potential of AI in patient education, ethics and the FDI communique on AI in dentistry. The review begins by elucidating the historical context of AI, outlining its recent widespread use in various sectors, including medicine and dentistry. The narrative delves into the fundamental concepts of AI, which entails developing machines capable of executing tasks that typically necessitate human intellect. In the biomedical realm, AI has evolved from exploring computational models to constructing systems for clinical data processing and interpretation, aiming to enhance medical/dental decision-making. The discussion delves into the pivotal role of AI models in dentistry, such as Large Language Models (LLM), Large Vision Models (LVM), and Multimodality Models (MM), revolutionizing processes from clinical documentation to treatment planning. The narrative extends to the applications of AI in dental specialties such as periodontics, endodontics, oral medicine and pathology, restorative dentistry, prosthodontics, paediatric dentistry, forensic odontology, oral and maxillofacial surgery, orthodontics, and orofacial pain management. AI's role in improving treatment outcomes, diagnostic accuracy, and decision-making processes is evident across these specialties, showcasing its potential in transforming dental care. The review concludes by highlighting the need for continued validation, interdisciplinary collaboration, and regulatory frameworks to ensure the seamless integration of AI into dentistry, paving the way for enhanced patient outcomes and evidence-based practice in the field.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Artificial intelligence has emerged as a promising clinical decision-support tool in dentistry.
“In this context, artificial intelligence (AI) has emerged as a promising clinical decision-support tool in dentistry [26][27][28].”
From Comparing Artificial Intelligence (ChatGPT, Gemini, DeepSeek) and Oral Surgeons in Detecting Clinically Relevant Drug–Drug Interactions in Dental Therapy (2025) · cites this paper as [26]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Overview of artificial intelligence in medicine
Amisha, Malik, P., Pathania, M., & Rathaur, V. K. (2019) · Journal of Family Medicine and Primary Care · doi:10.4103/jfmpc.jfmpc_440_19 · Cited by 1,106 · see citing works
Rokhshad, R., Salehi, S. N., Yavari, A., Shobeiri, P., Esmaeili, M., Manila, N., Motamedian, S. R., & Mohammad-Rahimi, H. (2024) · Oral Radiology · doi:10.1007/s11282-023-00715-5 · Cited by 23 · see citing works
Flagged first, then by citations
Research works printed but not linked: 119
- [1]Schwendicke F. Samek W. Krois J. Artificial intelligence in dentistry: chances and challenges J Dent Res 99 2020 769 774 10.1177/0022034520915714 32315260 PMC7309354 · DOI · Paper page
- [2]Russell Stuart J. Peter N. Artificial Intelligence: A Modern Approach 2016 Pearson London
- [4]November Joseph A. Biomedical Computing: Digitizing Life in the United States 2012 JHU Press Baltimore, MD
- [5]Ding H. Wu J. Zhao W. Matinlinna J.P. Burrow M.F. Tsoi J.K.H. Artificial intelligence in dentistry-A review Front Dent Med 4 2023 Feb 20 1085251 10.3389/fdmed.2023.1085251 PMC11811754 39935549 · DOI · Paper page
- [6]Meskó B. The impact of multimodal large language models on health care's future J Med Internet Res 25 2023 e52865 10.2196/52865 37917126 PMC10654899 · DOI · Paper page
- [7]Vijayakumar S. Magazzù G. Moon P. Occhipinti A. Angione C. A practical guide to integrating multimodal machine learning and metabolic modeling Methods Mol Biol 2399 2022 87 122 10.1007/978-1-0716-1831-8_5 35604554 · DOI
- [8]Thirunavukarasu A.J. Ting D.S.J. Elangovan K. Gutierrez L. Tan T.F. Ting D.S.W. Large language models in medicine Nat Med 29 8 2023 Aug 1930 1940 10.1038/s41591-023-02448-8 37460753 · DOI · Paper page
- [9]Suárez A. Díaz-Flores García V. Algar J. Gómez Sánchez M. Llorente de Pedro M. Freire Y. Unveiling the ChatGPT phenomenon: Evaluating the consistency and accuracy of endodontic question answers Int Endod J 57 1 2024 Jan 108 113 10.1111/iej.13985 37814369 · DOI
- [10]Rewthamrongsris P. Burapacheep J. Trachoo V. Porntaveetus T. Accuracy of large language models for infective endocarditis prophylaxis in dental procedures Int Dent J 75 2025 206 212 10.1016/j.identj.2024.09.033 39395898 PMC11806337 · DOI
- [11]Rischke R. Schneider L. Müller K. Samek W. Schwendicke F. Krois J. Federated Learning in Dentistry: Chances and Challenges J Dent Res 101 11 2022 Oct 1269 1273 10.1177/00220345221108953 35912725 PMC9516599 · DOI
- [12]Schneider L. Rischke R. Krois J. Krasowski A. Büttner M. Mohammad-Rahimi H. Chaurasia A. Pereira N.S. Lee J.H. Uribe S.E. Shahab S. Koca-Ünsal R.B. Ünsal G. Martinez-Beneyto Y. Brinz J. Tryfonos O. Schwendicke F. Federated vs Local vs Central Deep Learning of Tooth Segmentation on Panoramic Radiographs J Dent 135 2023 Aug 104556 10.1016/j.jdent.2023.104556 37209769 · DOI
- [13]Felsch Marco Meyer Ole Schlickenrieder Anne Engels Paula Schönewolf Jule Zöllner Felicitas Heinrich-Weltzien Roswitha Hesenius Marc Hickel Reinhard Gruhn Volker Kühnisch Jan Detection and localization of caries and hypomineralization on dental photographs with a vision transformer model npj Digit. Med 6 2023 198 10.1038/s41746-023-00944-2 37880375 PMC10600213 · DOI
- [14]Al-Hamadani M.N.A. Fadhel M.A. Alzubaidi L. Harangi B. Reinforcement learning algorithms and applications in healthcare and robotics: a comprehensive and systematic review Sensors 24 2024 2461 38676080 PMC11053800
- [15]Bahrami R. Pourhajibagher M. Nikparto N. Bahador A. Robot-assisted dental implant surgery procedure: a literature review J Dent Sci 19 2024 1359 1368 10.1016/j.jds.2024.03.011 39035318 PMC11259664 · DOI
- [16]Khurshid Z. Digital dentistry: transformation of oral health and dental education with technology Eur J Dent 17 2023 943 944 10.1055/s-0043-1772674 37729928 PMC10756720 · DOI
- [17]Srivastava B. Chandra S. Singh S. Srivastava T. Artificial intelligence in dentistry: it's applications, impact and challenges Asian J Oral Health Allied Sci 13 2023 7 10.25259/AJOHAS_10_2023 · DOI
- [18]Zheng Z. Yan H. Setzer F.C. Shi K.J. Mupparapu M. Li J. Anatomically Constrained Deep Learning for Automating Dental CBCT Segmentation and Lesion Detection IEEE Transactions on Automation Science and Engineering 18 2 2021 603 614 10.1109/TASE.2020.3025871 Article 9219218 · DOI
- [19]Papantonopoulos G. Takahashi K. Bountis T. Loos B. Using cellular automata experiments to model periodontitis: a first step towards understanding the nonlinear dynamics of the disease Int J Bifurcation Chaos 23 3 2012 2417 10.1142/S0218127413500569 · DOI
- [20]Ossowska A. Kusiak A. Świetlik D. Evaluation of the progression of periodontitis with the use of neural networks J Clin Med 11 2022 4667 36012906 PMC9409699
- [21]Krois J. Ekert T. Meinhold L. Golla T. Kharbot B. Wittemeier A. Dörfer C. Schwendicke F. Deep Learning for the Radiographic Detection of Periodontal Bone Loss Sci Rep 9 1 2019 Jun 11 8495 10.1038/s41598-019-44839-3 31186466 PMC6560098 · DOI
- [22]Thanathornwong B. Suebnukarn S. Automatic detection of periodontal compromised teeth in digital panoramic radiographs using faster regional convolutional neural networks Imaging Sci Dent 50 2020 169 174 10.5624/isd.2020.50.2.169 32601592 PMC7314603 · DOI
- [23]Khubrani Y.H. Thomas D. Slator P.J. White R.D. Farnell D.J.J. Detection of periodontal bone loss and periodontitis from 2D dental radiographs via machine learning and deep learning: systematic review employing APPRAISE-AI and meta-analysis Dentomaxillofac Radiol 54 2 2025 89 108 10.1093/dmfr/twae070 39656957 PMC11979759 · DOI
- [24]Shetty S. Talaat W. AlKawas S. Al-Rawi N. Reddy S. Hamdoon Z. Kheder W. Acharya A. Ozsahin D.U. David LR. Application of artificial intelligence-based detection of furcation involvement in mandibular first molar using cone beam tomography images- a preliminary study BMC Oral Health 24 1 2024 Dec 4 1476 10.1186/s12903-024-05268-5 39633335 PMC11619149 · DOI
- [25]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. Kim T.I. Yi WJ. Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis Sci Rep 10 1 2020 May 5 7531 10.1038/s41598-020-64509-z PMID: 32372049; PMCID: PMC7200807 32372049 PMC7200807 · DOI
- [26]Lee J.H. Kim D.H. Jeong S.N. Choi S.H. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm J Periodontal Implant Sci 48 2018 114 123 10.5051/jpis.2018.48.2.114 29770240 PMC5944222 · DOI
- [27]Santamaria P. Troiano G. Serroni M. Araùjo T.G. Ravidà A. Nibali L. Exploring the accuracy of tooth loss prediction between a clinical periodontal prognostic system and a machine learning prognostic model Journal of Clinical Periodontology 51 10 2024 1333 1341 10.1111/jcpe.14023 39109394 · DOI
- [28]Zhao A. Chen Y. Yang H. Chen T. Rao X. Li Z. Exploring the risk factors and clustering patterns of periodontitis in patients with different subtypes of diabetes through machine learning and cluster analysis Acta Odontol Scand 83 2024 Dec 3 653 665 10.2340/aos.v83.42435 39632553 PMC11633034 · DOI
- [29]Li Y. Zhang B. Li D. Zhang Y. Xue Y. Hu K. Machine Learning and Mendelian Randomization Reveal Molecular Mechanisms and Causal Relationships of Immune-Related Biomarkers in Periodontitis Mediators Inflamm 2024 2024 Dec 16 9983323 10.1155/mi/9983323 PMC11666315 39717623 · DOI
- [30]Tastan Eroglu Z. Babayigit O. Ozkan Sen D. Ucan Yarkac F. Performance of ChatGPT in classifying periodontitis according to the 2018 classification of periodontal diseases Clin Oral Investig 28 2024 407 10.1007/s00784-024-05799-9 PMC11217036 38951256 · DOI
- [31]Chatzopoulos G.S. Koidou V.P. Tsalikis L. Kaklamanos EG. Large language models in periodontology: Assessing their performance in clinically relevant questions J Prosthet Dent 2024 Nov 18 10.1016/j.prosdent.2024.10.020 S0022-3913(24)00714-5 39562221 · DOI
- [32]Fanelli F. Saleh M. Santamaria P. Zhurakivska K. Nibali L. Troiano G. Development and Comparative Evaluation of a Reinstructed GPT-4o Model Specialized in Periodontology J Clin Periodontol 26 2024 Dec 10.1111/jcpe.14101 PMC12003056 39723544 · DOI
- [33]Yauney G. Rana A. Wong L.C. Javia P. Muftu A. Shah P. Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health Annu Int Conf IEEE Eng Med Biol Soc 2019 2019 Jul 3387 3393 10.1109/EMBC.2019.8857965 31946607 · DOI
- [34]Suh B. Yu H. Cha J.K. Choi J. Kim J.W. Explainable deep learning approaches for risk screening of periodontitis J Dent Res 104 2025 45 53 10.1177/00220345241286488 39563207 PMC11667192 · DOI
- [35]Chapman M.N. Nadgir R.N. Akman A.S. Saito N. Sekiya K. Kaneda T. Sakai O. Periapical lucency around the tooth: radiologic evaluation and differential diagnosis Radiographics 33 1 2013 Jan-Feb E15 E32 10.1148/rg.331125172 23322846 · DOI
- [36]Setzer F.C. Shi K.J. Zhang Z. Yan H. Yoon H. Mupparapu M. Li J. Artificial Intelligence for the Computer-aided Detection of Periapical Lesions in Cone-beam Computed Tomographic Images J Endod 46 7 2020 Jul 987 993 10.1016/j.joen.2020.03.025 32402466 · DOI
- [37]Leonardi Dutra K. Haas L. Porporatti A.L. Flores-Mir C. Nascimento Santos J. Mezzomo L.A. Corrêa M. De Luca Canto G. Diagnostic Accuracy of Cone-beam Computed Tomography and Conventional Radiography on Apical Periodontitis: A Systematic Review and Meta-analysis J Endod 42 3 2016 Mar 356 364 10.1016/j.joen.2015.12.015 26902914 · DOI
- [38]Orhan K. Bayrakdar I.S. Ezhov M. Kravtsov A. Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans Int Endod J 53 2020 680 689 10.1111/iej.13265 31922612 · DOI
- [39]Endres M.G. Hillen F. Salloumis M. Sedaghat A.R. Niehues S.M. Quatela O. Hanken H. Smeets R. Beck-Broichsitter B. Rendenbach C. Lakhani K. Heiland M. Gaudin RA. Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs Diagnostics (Basel) 10 6 2020 Jun 24 430 10.3390/diagnostics10060430 32599942 PMC7344682 · DOI
- [40]Bayrakdar I.S. Orhan K. Çelik Ö Bilgir E. Sağlam H. Kaplan F.A. Görür S.A. Odabaş A. Aslan A.F. Różyło-Kalinowska I. A U-Net Approach to Apical Lesion Segmentation on Panoramic Radiographs Biomed Res Int 2022 2022 Jan 15 7035367 10.1155/2022/7035367 PMC8783705 35075428 · DOI
- [41]Song I.S. Shin H.K. Kang J.H. Kim J.E. Huh K.H. Yi W.J. Lee S.S. Heo M.S. Deep learning-based apical lesion segmentation from panoramic radiographs Imaging Sci Dent 52 4 2022 Dec 351 357 10.5624/isd.20220078 36605863 PMC9807797 · DOI
- [42]Li S. Liu J. Zhou Z. Zhou Z. Wu X. Li Y. Wang S. Liao W. Ying S. Zhao Z. Artificial intelligence for caries and periapical periodontitis detection J Dent 122 2022 Jul 104107 10.1016/j.jdent.2022.104107 35341892 · DOI
- [43]Hamdan M.H. Tuzova L. Mol A. Tawil P.Z. Tuzoff D. Tyndall DA. The effect of a deep-learning tool on dentists' performances in detecting apical radiolucencies on periapical radiographs Dentomaxillofac Radiol 51 7 2022 Sep 1 20220122 10.1259/dmfr.20220122 PMC9522978 35980437 · DOI
- [44]Sadr S. Mohammad-Rahimi H. Motamedian S.R. Zahedrozegar S. Motie P. Vinayahalingam S. Dianat O. Nosrat A. Deep Learning for Detection of Periapical Radiolucent Lesions: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy J Endod 49 3 2023 Mar 248 261.e3 10.1016/j.joen.2022.12.007 36563779 · DOI
- [45]Slim M.L. Jacobs R. de Souza Leal R.M. Fontenele R.C. AI-driven segmentation of the pulp cavity system in mandibular molars on CBCT images using convolutional neural networks Clin Oral Investig 28 2024 650 10.1007/s00784-024-06009-2 PMC11582138 39570431 · DOI
- [46]Jin L. Zhou W. Tang Y. Yu Z. Fan J. Wang L. Liu C. Gu Y. Zhang P. Detection of C-shaped mandibular second molars on panoramic radiographs using deep convolutional neural networks Clin Oral Investig 28 12 2024 Nov 18 646 10.1007/s00784-024-06049-8 39557710 · DOI
- [47]Saghiri M.A. Garcia-Godoy F. Gutmann J.L. Lotfi M. Asgar K. The reliability of artificial neural network in locating minor apical foramen: a cadaver study J Endod 38 2012 1130 1134 10.1016/j.joen.2012.05.004 22794221 · DOI
- [48]Mendonça de Moura J.D. Fontana C.E. Reis da Silva Lima V.H. de Souza Alves I. André de Melo Santos P. de Almeida Rodrigues P. Comparative accuracy of artificial intelligence chatbots in pulpal and periradicular diagnosis: A cross-sectional study Comput Biol Med 183 2024 Dec 109332 10.1016/j.compbiomed.2024.109332 39471663 · DOI
- [49]Portilla N.D. Garcia-Font M. Nagendrababu V. Abbott P.V. Sanchez J.A.G. Abella F. Accuracy and Consistency of Gemini Responses Regarding the Management of Traumatized Permanent Teeth Dent Traumatol 26 2024 Oct 10.1111/edt.13004 39460511 · DOI
- [50]Litjens G. Kooi T. Bejnordi B.E. Setio A.A.A. Ciompi F. Ghafoorian M. van der Laak JAWM van Ginneken B. Sánchez C.I. A survey on deep learning in medical image analysis Med Image Anal 42 2017 Dec 60 88 10.1016/j.media.2017.07.005 28778026 · DOI · Paper page
- [51]Aubreville M. Knipfer C. Oetter N. Jaremenko C. Rodner E. Denzler J. Bohr C. Neumann H. Stelzle F. Maier A. Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning Sci Rep 7 1 2017 Sep 20 11979 10.1038/s41598-017-12320-8 28931888 PMC5607286 · DOI
- [52]Li C. Jing B. Ke L. Li B. Xia W. He C. Qian C. Zhao C. Mai H. Chen M. Cao K. Mo H. Guo L. Chen Q. Tang L. Qiu W. Yu Y. Liang H. Huang X. Liu G. Li W. Wang L. Sun R. Zou X. Guo S. Huang P. Luo D. Qiu F. Wu Y. Hua Y. Liu K. Lv S. Miao J. Xiang Y. Sun Y. Guo X. Lv X. Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies Cancer Commun (Lond) 38 1 2018 Sep 25 59 10.1186/s40880-018-0325-9 30253801 PMC6156962 · DOI
- [53]Mascharak S. Baird B.J. Holsinger F.C. Detecting oropharyngeal carcinoma using multispectral, narrow-band imaging and machine learning Laryngoscope 128 2018 2514 2520 10.1002/lary.27159 29577322 · DOI
- [54]Moccia S. De Momi E. Guarnaschelli M. Savazzi M. Laborai A. Guastini L. Peretti G. Mattos L.S. Confident texture-based laryngeal tissue classification for early stage diagnosis support J Med Imaging (Bellingham) 4 3 2017 Jul 034502 10.1117/1.JMI.4.3.034502 PMC5621380 28983494 · DOI
- [55]Achararit P. Manaspon C. Jongwannasiri C. Phattarataratip E. Osathanon T. Sappayatosok K. Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks Eur J Dent 17 4 2023 Oct 1275 1282 10.1055/s-0042-1760300 36669652 PMC10756816 · DOI
- [56]Warin K. Limprasert W. Suebnukarn S. Jinaporntham S. Jantana P. Performance of deep convolutional neural network for classification and detection of oral potentially malignant disorders in photographic images Int J Oral Maxillofac Surg 51 5 2022 May 699 704 10.1016/j.ijom.2021.09.001 34548194 · DOI
- [57]Song B. Sunny S. Uthoff R.D. Patrick S. Suresh A. Kolur T. Keerthi G. Anbarani A. Wilder-Smith P. Kuriakose M.A. Birur P. Rodriguez J.J. Liang R. Automatic classification of dual-modalilty, smartphone-based oral dysplasia and malignancy images using deep learning Biomed Opt Express 9 11 2018 Oct 10 5318 5329 10.1364/BOE.9.005318 30460130 PMC6238918 · DOI
- [58]Mahmood H. Shaban M. Indave B.I. Santos-Silva A.R. Rajpoot N. Khurram S.A. Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review Oral Oncol 110 2020 Nov 104885 10.1016/j.oraloncology.2020.104885 32674040 · DOI
- [59]Charoenlarp P. Silkosessak-Chaiudom O. Vipismakul V. Atypical periosteal reaction and unusual bone involvement of ameloblastoma: a case report with 8-year follow-up Imaging Sci Dent 51 2021 195 201 10.5624/isd.20200264 34235065 PMC8219456 · DOI
- [60]Lu C. Lewis J.S. Jr. Dupont W.D. Plummer W.D. Jr. Janowczyk A. Madabhushi A. An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival Mod Pathol 30 12 2017 Dec 1655 1665 10.1038/modpathol.2017.98 28776575 PMC6128166 · DOI
- [61]Wu B. Khong P.L. Chan T. Automatic detection and classification of nasopharyngeal carcinoma on PET/CT with support vector machine Int J Comput Assist Radiol Surg 7 2012 635 646 10.1007/s11548-011-0669-y 22215412 · DOI
- [63]Giraldo-Roldán D. Araújo A.L.D. Moraes M.C. da Silva V.M. Ribeiro E.C.C. Cerqueira M. Saldivia-Siracusa C. Sousa-Neto S.S. Pérez-de-Oliveira M.E. Lopes M.A. Kowalski L.P. de Carvalho ACPLF Santos-Silva A.R. Vargas P.A. Artificial intelligence and radiomics in the diagnosis of intraosseous lesions of the gnathic bones: A systematic review J Oral Pathol Med 53 7 2024 Aug 415 433 10.1111/jop.13548 38807455 · DOI
- [64]Baik J. Ye Q. Zhang L. Poh C. Rosin M. MacAulay C. Guillaud M. Automated classification of oral premalignant lesions using image cytometry and Random Forests-based algorithms Cell Oncol (Dordr) 37 3 2014 Jun 193 202 10.1007/s13402-014-0172-x 24817187 PMC13004423 · DOI
- [65]Zhang L. Wu Y. Zheng B. Su L. Chen Y. Ma S. Hu Q. Zou X. Yao L. Yang Y. Chen L. Mao Y. Chen Y. Ji M. Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated Raman scattering microscopy Theranostics 9 9 2019 Apr 13 2541 2554 10.7150/thno.32655 31131052 PMC6526002 · DOI
- [66]Fouad S. Randell D. Galton A. Mehanna H. Landini G. Unsupervised morphological segmentation of tissue compartments in histopathological images PLoS One 12 11 2017 Nov 30 e0188717 10.1371/journal.pone.0188717 PMC5708642 29190786 · DOI
- [67]Halicek M. Shahedi M. Little J.V. Chen A.Y. Myers L.L. Sumer B.D. Fei B. Head and Neck Cancer Detection in Digitized Whole-Slide Histology Using Convolutional Neural Networks Sci Rep. 9 1 2019 Oct 1 14043 10.1038/s41598-019-50313-x 31575946 PMC6773771 · DOI
- [68]Christie B. Musri N. Djustiana N. Takarini V. Tuygunov N. Zakaria M.N. Cahyanto A. Advances and challenges in regenerative dentistry: A systematic review of calcium phosphate and silicate-based materials on human dental pulp stem cells Mater Today Bio. 23 2023 Sep 23 100815 10.1016/j.mtbio.2023.100815 PMC10539671 37779917 · DOI
- [69]Fukuda M. Inamoto K. Shibata N. Ariji Y. Yanashita Y. Kutsuna S. Nakata K. Katsumata A. Fujita H. Ariji E. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography Oral Radiol 36 4 2020 Oct 337 343 10.1007/s11282-019-00409-x 31535278 · DOI
- [70]Magat G. Altındag A. Pertek Hatipoglu F. Hatipoglu O. Bayrakdar İS Celik O. Orhan K. Automatic deep learning detection of overhanging restorations in bitewing radiographs Dentomaxillofac Radiol 53 7 2024 Oct 1 468 477 10.1093/dmfr/twae036 39024043 PMC11440037 · DOI
- [71]Jaiswal P. Bhirud S.G. Study and Analysis of an Approach Towards the Classification of Tooth Wear in Dentistry Using Machine Learning Technique 2021 IEEE International Conference on Technology, Research, and Innovation for Betterment of Society (TRIBES) 2021 1 6
- [72]Lee J.H. Kim D.H. Jeong S.N. Choi S.H. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm J Dent 77 2018 106 111 10.1016/j.jdent.2018.07.015 30056118 · DOI
- [73]Tuygunov N. Khairunnisa Z. Yahya N.A. Aziz A.A. Zakaria M.N. Israilova N.A. Cahyanto A. Bioactivity and remineralization potential of modified glass ionomer cement: A systematic review of the impact of calcium and phosphate ion release Dent Mater J 43 1 2024 Jan 30 1 10 10.4012/dmj.2023-132 38220163 · DOI
- [74]Khurshid Z. Waqas M. Hasan S. Kazmi S. Faheemuddin M. Deep learning architecture to infer Kennedy classification of partially edentulous arches using object detection techniques and piecewise annotations Int Dent J 75 2025 223 235 10.1016/j.identj.2024.11.005 39645471 PMC11806322 · DOI
- [75]Murata M. Ariji Y. Ohashi Y. Kawai T. Fukuda M. Funakoshi T. Kise Y. Nozawa M. Katsumata A. Fujita H. Ariji E. Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography Oral Radiol 35 3 2019 Sep 301 307 10.1007/s11282-018-0363-7 30539342 · DOI
- [76]Vinayahalingam S. Xi T. Bergé S. Maal T. de Jong G. Automated detection of third molars and mandibular nerve by deep learning Sci Rep 9 2019 9007 10.1038/s41598-019-45487-3 31227772 PMC6588560 · DOI
- [77]Chen Y. Lee J.K.Y. Kwong G. Pow E.H.N. Tsoi J.K.H. Morphology and fracture behavior of lithium disilicate dental crowns designed by human and knowledge-based AI J Mech Behav Biomed Mater 131 2022 105256 10.1016/j.jmbbm.2022.105256 35508087 · DOI
- [78]Yamaguchi S. Lee C. Karaer O. Ban S. Mine A. Imazato S. Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI J Dent Res 98 11 2019 Oct 1234 1238 10.1177/0022034519867641 31379234 · DOI
- [79]Baaj R.E. Alangari T.A. Artificial intelligence applications in smile design dentistry: A scoping review J Prosthodont 9 2024 Dec 10.1111/jopr.14000 39654301 · DOI
- [80]Ceylan G. Özel G.S. Memişoglu G. Emir F. Şen S. Evaluating the Facial Esthetic Outcomes of Digital Smile Designs Generated by Artificial Intelligence and Dental Professionals Applied Sciences 13 15 2023 9001 10.3390/app13159001 · DOI
- [81]Hwang, Jyh-Jing, Azernikov, Sergei, Efros, Alexei, Yu, Stella. (2018). Learning Beyond Human Expertise with Generative Models for Dental Restorations. 10.48550/arXiv.1804.00064. · DOI
- [82]Tian S. Wang M. Dai N. Ma H. Li L. Fiorenza L. Sun Y. Li Y. DCPR-GAN: Dental Crown Prosthesis Restoration Using Two-Stage Generative Adversarial Networks IEEE J Biomed Health Inform 26 1 2022 Jan 151 160 10.1109/JBHI.2021.3119394 34637385 · DOI
- [83]Ding H. Cui Z. Maghami E. Chen Y. Matinlinna J.P. Pow E.H.N. Fok A.S.L. Burrow M.F. Wang W. Tsoi J.K.H. Morphology and mechanical performance of dental crown designed by 3D-DCGAN Dent Mater 39 3 2023 Mar 320 332 10.1016/j.dental.2023.02.001 36822895 · DOI
- [84]Cho J.H. Yi Y. Choi J. Ahn J. Yoon H.I. Yilmaz B. Time efficiency, occlusal morphology, and internal fit of anatomic contour crowns designed by dental software powered by generative adversarial network: A comparative study J Dent 138 2023 Nov 104739 10.1016/j.jdent.2023.104739 37804938 · DOI
- [85]Shetty S. Gali S. Augustine D. Sv S. Artificial intelligence systems in dental shade-matching: a systematic review J Prosthodont 33 2024 519 532 10.1111/jopr.13805 37986239 · DOI
- [86]Kong H.-J. Kim Y.-L. Application of artificial intelligence in dental crown prosthesis: a scoping review BMC Oral Health 24 2024 937 10.1186/s12903-024-04657-0 39138474 PMC11321175 · DOI
- [87]Ali M.A. Fujita D. Kobashi S. Teeth and prostheses detection in dental panoramic X-rays using CNN-based object detector and a priori knowledge-based algorithm Sci Rep 13 2023 16542 10.1038/s41598-023-43591-z 37783773 PMC10545749 · DOI
- [88]Alharbi N. Alharbi A.S. AI-driven innovations in pediatric dentistry: enhancing care and improving outcome Cureus 16 2024 e69250 10.7759/cureus.69250 39398765 PMC11470390 · DOI
- [89]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 5 3 2020 Jul 233 243 10.1177/2380084419885612 31710817 PMC7298887 · DOI
- [90]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 102 9 2023 Aug 999 1006 10.1177/00220345231170535 37246832 · DOI
- [91]Qu X. Zhang C. Houser S.H. Zhang J. Zou J. Zhang W. Zhang Q. Prediction model for early childhood caries risk based on behavioral determinants using a machine learning algorithm Comput Methods Programs Biomed 227 2022 Dec 107221 10.1016/j.cmpb.2022.107221 36384058 · DOI
- [92]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 20 2020 141 10.1186/s12903-020-01114-6 32404094 PMC7222297 · DOI
- [93]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 46 2024 332 336 39420489
- [94]Mertens S. Krois J. Cantu A.G. Arsiwala L.T. Schwendicke F. Artificial intelligence for caries detection: randomized trial J Dent 115 2021 103849 10.1016/j.jdent.2021.103849 34656656 · DOI
- [95]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 1 6 2022 e0000046 10.1371/journal.pdig.0000046 PMC9645586 36381137 · DOI
- [96]Schwarzmaier J. Frenkel E. Neumayr J. Ammar N. Kessler A. Schwendicke F. Kühnisch J. Dujic H. Validation of an Artificial Intelligence-Based Model for Early Childhood Caries Detection in Dental Photographs J Clin Med 13 17 2024 Sep 3 5215 10.3390/jcm13175215 39274428 PMC11396146 · DOI
- [97]Elkarmi R. Abu-Ghazaleh S. Sonbol H. Haha O. AL-Haddad A. Hassona Y. ChatGPT for parents’ education about early childhood caries: A friend or foe? Int J Paediatr Dent 00 2024 1 8 10.1111/ipd.13283 39533165 · DOI
- [98]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 48 3 2024 May 24 30 10.22514/jocpd.2024.055 38755978 · DOI
- [99]Niño-Sandoval T.C. Guevara Pérez S.V. González F.A. Jaque R.A. Infante-Contreras C. Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and III Forensic Sci Int 281 2017 187.e181 187.e187 10.1016/j.forsciint.2017.10.004 29126697 · DOI
- [100]Ferraz A.X. Schroder ÂGD Gonçalves F.M. Küchler E.C. Santos R.S. Zeigelboim B.S. Pezzin A.P.T. Taveira K.V. Abuabara A. Baratto-Filho F. de Araujo CM. Artificial intelligence model for predicting sexual dimorphism through the hyoid bone in adult patients PLoS One 19 11 2024 Nov 19 e0310811 10.1371/journal.pone.0310811 PMC11575798 39561146 · DOI
- [101]Keyrouz Y. Saade M. Nahas Gholmieh M. Saadé A. A machine learning approach for age prediction based on trigeminal landmarks J Foren Legal Med 107 2024 102742 10.1016/j.jflm.2024.102742 39213905 · DOI
- [102]Bizjak Ž. Robič T. Dentage: deep learning for automated age prediction using panoramic dental X-ray images J Foren Sci 69 2024 2069 2074 10.1111/1556-4029.15629 39294554 · DOI
- [103]Matsuda S. Miyamoto T. Yoshimura H. Hasegawa T. Personal identification with orthopantomography using simple convolutional neural networks: a preliminary study Scientific Reports 10 2020 13559 10.1038/s41598-020-70474-4 32782269 PMC7419525 · DOI
- [104]Macrì M. D'Albis V. D'Albis G. Forte M. Capodiferro S. Favia G. Alrashadah A.O. García V.D. Festa F. The Role and Applications of Artificial Intelligence in Dental Implant Planning: A Systematic Review Bioengineering (Basel) 11 8 2024 Jul 31 778 10.3390/bioengineering11080778 39199736 PMC11351972 · DOI
- [105]Berends B. Vinayahalingam S. Baan F. Flügge T. Maal T. Bergé S. de Jong G. Xi T. Automated condylar seating assessment using a deep learning-based three-step approach Clin Oral Investig 28 9 2024 Sep 4 512 10.1007/s00784-024-05895-w PMC11371884 39227487 · DOI
- [106]Thompson R.F. Valdes G. Fuller C.D. Carpenter C.M. Morin O. Aneja S. Lindsay W.D. Aerts HJWL Agrimson B. Deville C. Jr. Rosenthal S.A. Yu J.B. Thomas C.R. Jr Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation? Radiother Oncol 129 3 2018 Dec 421 426 10.1016/j.radonc.2018.05.030 29907338 PMC9620952 · DOI
- [107]Chang J.S. Ma C.Y. Ko E.W.C. Prediction of surgery-first approach orthognathic surgery using deep learning models Int J Oral Maxillofac Surg 53 2024 942 949 10.1016/j.ijom.2024.05.003 38821731 · DOI
- [108]Satapathy S.K. Kunam A. Rashme R. Sudarsanam P.P. Gupta A. Kumar H.S.K. AI-Assisted Treatment Planning for Dental Implant Placement: Clinical vs AI-Generated Plans J Pharm Bioallied Sci 16 Suppl 1 2024 10.4103/jpbs.jpbs_1121_23 FebS939–S941 PMC11001018 38595502 · DOI
- [109]Azadi A. Gorjinejad F. Mohammad-Rahimi H. Tabrizi R. Alam M. Golkar M. Evaluation of AI-generated responses by different artificial intelligence chatbots to the clinical decision-making case-based questions in oral and maxillofacial surgery Oral Surg Oral Med Oral Pathol Oral Radiol 137 6 2024 Jun 587 593 10.1016/j.oooo.2024.02.018 38570273 · DOI
- [110]Wu Y. Zou S. Lv P. Wang X. Accuracy of an autonomous dental implant robotic system in dental implant surgery J Prosthet Dent 2024 Aug 13 10.1016/j.prosdent.2024.07.020 S0022-3913(24)00501-8 39142994 · DOI
- [111]Zhao Z. Zhang Y. Lin L. Huang W. Xiao C. Liu J. Chai G. Intelligent electromagnetic navigation system for robot-assisted intraoral osteotomy in mandibular tumor resection: a model experiment Front Immunol 15 2024 Jul 25 1436276 10.3389/fimmu.2024.1436276 PMC11306084 39119338 · DOI
- [112]Ye H. Cheng Z. Ungvijanpunya N. Chen W. Cao L. Gou Y. Is automatic cephalometric software using artificial intelligence better than orthodontist experts in landmark identification? BMC Oral Health 23 1 2023 Jul 8 467 10.1186/s12903-023-03188-4 37422630 PMC10329795 · DOI
- [113]Londono J. Ghasemi S. Hussain Shah A. Fahimipour A. Ghadimi N. Hashemi S. Khurshid Z. Dashti M. Evaluation of deep learning and convolutional neural network algorithms accuracy for detecting and predicting anatomical landmarks on 2D lateral cephalometric images: A systematic review and meta-analysis Saudi Dent J 35 5 2023 Jul 487 497 10.1016/j.sdentj.2023.05.014 37520606 PMC10373073 · DOI
- [114]Noeldeke B. Vassis S. Sefidroodi M. Pauwels R. Stoustrup P. Comparison of deep learning models to detect crossbites on 2D intraoral photographs Head Face Med 20 2024 45 10.1186/s13005-024-00448-8 39223562 PMC11367978 · DOI
- [115]Etemad L.E. Heiner J.P. Amin A.A. Wu T.H. Chao W.L. Hsieh S.J. Sun Z. Guez C. Ko CC. Effectiveness of Machine Learning in Predicting Orthodontic Tooth Extractions: A Multi-Institutional Study Bioengineering (Basel) 11 9 2024 Aug 31 888 10.3390/bioengineering11090888 39329630 PMC11429385 · DOI
- [116]Köktürk B. Pamukçu H. Gözüaçık Ö. Evaluation of different machine learning algorithms for extraction decision in orthodontic treatment Orthod Craniofac Res 27 Suppl 2 2024 13 24 10.1111/ocr.12811 38764408 PMC11654355 · DOI
- [117]Kreiner M. Viloria J. A novel artificial neural network for the diagnosis of orofacial pain and temporomandibular disorders J Oral Rehabil 49 2022 884 889 10.1111/joor.13350 35722743 · DOI
- [118]Latypov T.H. So M.C. Hung P.S. Tsai P. Walker M.R. Tohyama S. Tawfik M. Rudzicz F. Hodaie M. Brain imaging signatures of neuropathic facial pain derived by artificial intelligence Sci Rep 13 1 2023 Jul 3 10699 10.1038/s41598-023-37034-y 37400574 PMC10318015 · DOI
- [119]de Araujo B.M.M. de Jesus Freitas P.F. Deliga Schroder A.G. Küchler E.C. Baratto-Filho F. Ditzel Westphalen V.P. Carneiro E Xavier da Silva-Neto U. de Araujo C.M. PAINe: An Artificial Intelligence-based Virtual Assistant to Aid in the Differentiation of Pain of Odontogenic versus Temporomandibular Origin J Endod 50 12 2024 Dec 1761 1765.e2 10.1016/j.joen.2024.09.008 39342988 · DOI
- [120]Li Z. Jia Y. Li Y. Han D. Automatic prediction of obstructive sleep apnea event using deep learning algorithm based on ECG and thoracic movement signals Acta Otolaryngol 144 2024 52 57 10.1080/00016489.2024.2301732 38240117 · DOI
- [121]Martinot J.B. Le-Dong N.N. Cuthbert V. Denison S. Gozal D. Lavigne G. Pépin JL. Artificial Intelligence Analysis of Mandibular Movements Enables Accurate Detection of Phasic Sleep Bruxism in OSA Patients: A Pilot Study Nat Sci Sleep 13 2021 Aug 23 1449 1459 10.2147/NSS.S320664 34466045 PMC8397703 · DOI
Cited by
Sorted by citations
Tayeb, S., Barausse, C., Pellegrino, G., Sansavini, M., Pistilli, R., & Felice, P. (2025) · Applied Sciences · doi:10.3390/app152312851 · Cited by 12 · see citing works
- Authors
- Samaranayake, L., Tuygunov, N., Schwendicke, F., Osathanon, T., Khurshid, Z., Boymuradov, S. A., & Cahyanto, A.
- Affiliations
- Chulalongkorn University (TH)
- Kimyo International University in Tashkent (UZ)
- Ludwig-Maximilians-Universität München (DE)
- Tashkent Medical Academy (UZ)
- Ajman University (AE)
- Corresponding author
- Samaranayake, Lakshman
- Tuygunov, Nozimjon
- ORCID
- Venue
- International Dental Journal
- Publisher
- Elsevier BV
- Published
- 2025-04
- Volume
- 75
- Issue
- 2
- Pages
- 383-396
- ISSN
- 0020-6539 (ISSN-L)
- PMC
- PMC11976540
- OpenAlex
- W4408306782
- Cited by
- 240
- References
- 2
- Citations by year
- 2026: 153 · 2025: 87
- FWCI
- 14.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Dental Journal (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- Funders
- Chulalongkorn University
- Hong Kong University
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Dentistry › Oral Surgery › Dental Radiography and Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in dentistry, dental diagnosis, dental treatment planning, large language models, large vision models, periodontics, endodontics, restorative dentistry, prosthodontics, pediatric dentistry, forensic odontology, maxillofacial surgery, orthodontics, clinical decision-making, history of artificial intelligence
- MeSH terms
- Clinical Decision-Making, Artificial Intelligence, Dentistry, Diagnosis, Humans, Patient Care Planning, Treatment Outcome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review