The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry

Abstract

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.

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    Kong H.-J., Kim Y.-L. Application of artificial intelligence in dental crown prosthesis: a scoping review. BMC Oral Health. 2024;24:937.

    Not in our library10.1186/s12903-024-04657-0

87. 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. 2023;13:16542.

    Not in our library10.1038/s41598-023-43591-z

88. 88.

    Alharbi N., Alharbi A.S. AI-driven innovations in pediatric dentistry: enhancing care and improving outcome. Cureus. 2024;16:e69250.

    Not in our library10.7759/cureus.69250

89. 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. 2020 Jul;5(3):233-243.

    Not in our library10.1177/2380084419885612

90. 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. 2023 Aug;102(9):999-1006.

    Not in our library10.1177/00220345231170535

91. 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. 2022 Dec;227.

    Not in our library10.1016/j.cmpb.2022.107221

92. 92.
    Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments · 2020

    Not checked
93. 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. 2024;46:332-336.

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94. 94.

    Mertens S., Krois J., Cantu A.G., Arsiwala L.T., Schwendicke F. Artificial intelligence for caries detection: randomized trial. J Dent. 2021;115.

    Not in our library10.1016/j.jdent.2021.103849

95. 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. 2022;1(6).

    Not in our library10.1371/journal.pdig.0000046

96. 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. 2024 Sep 3;13(17):5215.

    Not in our library10.3390/jcm13175215

97. 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. 2024;00:1-8.

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98. 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. 2024 May;48(3):24-30.

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99. 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. 2017;281:187.e181-187.e187.

    Not in our library10.1016/j.forsciint.2017.10.004

100. 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. 2024 Nov 19;19(11).

     Not in our library10.1371/journal.pone.0310811

101. 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. 2024;107.

     Not in our library10.1016/j.jflm.2024.102742

102. 102.

     Bizjak Ž., Robič T. Dentage: deep learning for automated age prediction using panoramic dental X-ray images. J Foren Sci. 2024;69:2069-2074.

     Not in our library10.1111/1556-4029.15629

103. 103.

     Matsuda S., Miyamoto T., Yoshimura H., Hasegawa T. Personal identification with orthopantomography using simple convolutional neural networks: a preliminary study. Scientific Reports. 2020;10:13559.

     Not in our library10.1038/s41598-020-70474-4

104. 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). 2024 Jul 31;11(8):778.

     Not in our library10.3390/bioengineering11080778

105. 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. 2024 Sep 4;28(9):512.

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106. 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., Rosenthal S.A., Yu J.B., Thomas C.R. Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation? Radiother Oncol. 2018 Dec;129(3):421-426.

     Not in our library10.1016/j.radonc.2018.05.030

107. 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. 2024;53:942-949.

     Not in our library10.1016/j.ijom.2024.05.003

108. 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. 2024;16(Suppl 1).

     Not in our library10.4103/jpbs.jpbs_1121_23

109. 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. 2024 Jun;137(6):587-593.

     Not in our library10.1016/j.oooo.2024.02.018

110. 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.

     Not in our library10.1016/j.prosdent.2024.07.020

111. 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. 2024 Jul 25;15.

     Not in our library10.3389/fimmu.2024.1436276

112. 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. 2023 Jul 8;23(1):467.

     Not in our library10.1186/s12903-023-03188-4

113. 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. 2023 Jul;35(5):487-497.

     Not in our library10.1016/j.sdentj.2023.05.014

114. 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. 2024;20:45.

     Not in our library10.1186/s13005-024-00448-8

115. 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). 2024 Aug 31;11(9):888.

     Not in our library10.3390/bioengineering11090888

116. 116.

     Köktürk B., Pamukçu H., Gözüaçık Ö. Evaluation of different machine learning algorithms for extraction decision in orthodontic treatment. Orthod Craniofac Res. 2024;27(Suppl 2):13-24.

     Not in our library10.1111/ocr.12811

117. 117.

     Kreiner M., Viloria J. A novel artificial neural network for the diagnosis of orofacial pain and temporomandibular disorders. J Oral Rehabil. 2022;49:884-889.

     Not in our library10.1111/joor.13350

118. 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. 2023 Jul 3;13(1):10699.

     Not in our library10.1038/s41598-023-37034-y

119. 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. 2024 Dec;50(12):1761-1765.e2.

     Not in our library10.1016/j.joen.2024.09.008

120. 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. 2024;144:52-57.

     Not in our library10.1080/00016489.2024.2301732

121. 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. 2021 Aug 23;13:1449-1459.

     Not in our library10.2147/nss.s320664

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