The role of artificial intelligence in shaping dentistry through advancement in data acquisition, clinical practice, education, and research

Abstract

Artificial intelligence (AI) is rapidly transforming dentistry through innovations in clinical practice, education, and research. Machine learning, and deep learning in particular, support disease diagnosis, treatment planning, prognosis prediction, and patient care. They do so by leveraging large-scale datasets from disciplines such as periodontology, endodontics, oral and maxillofacial surgery, orthodontics, and prosthodontics. This review first examines the technical and ethical foundations of dental AI, emphasizing data quality, representativeness, privacy protection, and standardized evaluation metrics. It then synthesizes current clinical applications, including AI-assisted diagnosis, image interpretation, surgical planning, robotic interventions, and predictive modeling of treatment outcomes. Educational uses of AI are examined in the context of simulation-based training, virtual reality integration, automated assessment, and patient education. The role of AI in scientific discovery is discussed with respect to data mining, biomarker identification, drug design, literature synthesis, and peer review support. Despite its potential, dental AI adoption faces challenges including limited and biased datasets, interoperability barriers, model interpretability, ethical concerns, and insufficient user training. Future integration will require rigorous external validation, regulatory compliance, seamless workflow incorporation, and interdisciplinary collaboration to ensure safety, transparency, and clinical utility. By addressing these limitations, AI can serve as a robust, multidimensional decision-support tool to improve diagnostic accuracy, personalizing care, and advancing dental education and research.

Keywords

Artificial intelligence / Clinical application / Data management / Dentistry

References

[1] Mohammad-Rahimi H, Sohrabniya F, Ourang SA, et al. Artificial intelligence in endodontics: Data preparation, clinical applications, ethical considerations, limitations, and future directions[J/OL]. International Endodontic Journal, 2024, 57(11): 1566-1595. https://pubmed.ncbi.nlm.nih.gov/39075670/. DOI: 10.1111/iej.14128.

[2] Farina R, Simonelli A, Trombelli L, et al. Emerging Applications of Digital Technologies for Periodontal Screening, Diagnosis and Prognosis in the Dental Setting[J/OL]. Journal of Clinical Periodontology, 2025, 52(S29): 211-245. https://pubmed.ncbi.nlm.nih.gov/40107262/. DOI: 10.1111/jcpe.14156.

[3] Lee SJ, Poon J, Jindarojanakul A, et al. Artificial intelligence in dentistry: Exploring emerging applications and future prospects[J/OL]. Journal of Dentistry, 2025, 155: 105648. https://pubmed.ncbi.nlm.nih.gov/39993553/. DOI: 10.1016/j.jdent.2025.105648.

[4] Najeeb M, Islam S . Artificial intelligence (AI) in restorative dentistry: current trends and future prospects[J/OL]. BMC Oral Health, 2025, 25(1): 592. https://pubmed.ncbi.nlm.nih.gov/40251567/. DOI: 10.1186/s12903-025-05989-1.

[5] Alessa N. Application of Artificial Intelligence in Pediatric Dentistry: A Literature Review[J/OL]. Journal of Pharmacy and Bioallied Sciences, 2024, 16(Suppl 3): S1938-S1940. https://pubmed.ncbi.nlm.nih.gov/39346390/. DOI: 10.4103/jpbs.jpbs_74_24.

[6] Chatzopoulos GS, Koidou VP, Tsalikis L, et al. Clinical Applications of Artificial Intelligence in Periodontology: A Scoping Review[J/OL]. Medicina, 2025, 61(6): 1066. https://pubmed.ncbi.nlm.nih.gov/40572754/. DOI: 10.3390/medicina61061066.

[7] Samaranayake L, Tuygunov N, Schwendicke F, et al. The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry[J/OL]. International Dental Journal, 2025, 75(2): 383-396. https://pubmed.ncbi.nlm.nih.gov/40074616/. DOI: 10.1016/j.identj.2025.02.005.

[8] Tuygunov N, Samaranayake L, Khurshid Z, et al. The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview Part 2: The Promise and Perils, and the International Dental Federation Communique[J/OL]. International Dental Journal, 2025, 75(2): 397-404. https://pubmed.ncbi.nlm.nih.gov/40011130/. DOI: 10.1016/j.identj.2025.02.006.

[9] Lin J, Liao Z, Dai J, et al. Digital and artificial intelligence-assisted cephalometric training effectively enhanced students' landmarking accuracy in preclinical orthodontic education[J/OL]. BMC Oral Health, 2025, 25(1): 623. https://pubmed.ncbi.nlm.nih.gov/40275285/. DOI: 10.1186/s12903-025-05978-4.

[10] Li Y, Ye H, Wu W, et al. Effectiveness and Methodologies of Virtual Reality Dental Simulators for Veneer Tooth Preparation Training: Randomized Controlled Trial[J/OL]. Journal of Medical Internet Research, 2025, 27: e63961. https://pubmed.ncbi.nlm.nih.gov/40402564/. DOI: 10.2196/63961.

[11] Khanagar SB, Alfadley A, Alfouzan K, et al. Developments and Performance of Artificial Intelligence Models Designed for Application in Endodontics: A Systematic Review[J/OL]. Diagnostics, 2023, 13(3): 414. https://pubmed.ncbi.nlm.nih.gov/36766519/. DOI: 10.3390/diagnostics13030414.

[12] Wang S, Wang Q, Zhao K, et al. Exploration of the shared diagnostic genes and mechanisms between periodontitis and primary Sjögren's syndrome by integrated comprehensive bioinformatics analysis and machine learning[J/OL]. International Immunopharmacology, 2024, 141: 112899. https://pubmed.ncbi.nlm.nih.gov/39142001/. DOI: 10.1016/j.intimp.2024.112899.

[13] Rokhshad R, Ducret M, Chaurasia A, et al. Ethical considerations on artificial intelligence in dentistry: A framework and checklist[J/OL]. Journal of Dentistry, 2023, 135: 104593. https://pubmed.ncbi.nlm.nih.gov/37355089/. DOI: 10.1016/j.jdent.2023.104593.

[14] Mörch CM, Atsu S, Cai W, et al. Artificial Intelligence and Ethics in Dentistry: A Scoping Review[J/OL]. Journal of Dental Research, 2021, 100(13): 1452-1460. https://pubmed.ncbi.nlm.nih.gov/34060359/. DOI: 10.1177/00220345211013808.

[15] Boege S, Milne-Ives M, Meinert E . Impact of Responsible AI on the Occurrence and Resolution of Ethical Issues: Protocol for a Scoping Review[J/OL]. JMIR Research Protocols, 2024, 13: e52349. https://pubmed.ncbi.nlm.nih.gov/38838329/. DOI: 10.2196/52349.

[16] Da Silva M, Horsley T, Singh D, et al. Legal concerns in health-related artificial intelligence: a scoping review protocol[J/OL]. Systematic Reviews, 2022, 11(1): 123. https://pubmed.ncbi.nlm.nih.gov/35715812/. DOI: 10.1186/s13643-022-01939-y.

[17] Li H, Xu Y, Lei Y, et al. Automatic Classification for Sagittal Craniofacial Patterns Based on Different Convolutional Neural Networks[J/OL]. Diagnostics, 2022, 12(6): 1359. https://pubmed.ncbi.nlm.nih.gov/35741169/. DOI: 10.3390/diagnostics12061359.

[18] Buyuk C, Arican Alpay B, Er F . Detection of the separated root canal instrument on panoramic radiograph: a comparison of LSTM and CNN deep learning methods[J/OL]. Dentomaxillofacial Radiology, 2023, 52(3): 20220209. https://pubmed.ncbi.nlm.nih.gov/36688738/. DOI: 10.1259/dmfr.20220209.

[19] Caicedo JC, Goodman A, Karhohs KW, et al. Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl[J/OL]. Nature Methods, 2019, 16(12): 1247-1253. https://pubmed.ncbi.nlm.nih.gov/31636459/. DOI: 10.1038/s41592-019-0612-7.

[20] Bayati M, Alizadeh Savareh B, Ahmadinejad H, et al. Advanced AI-driven detection of interproximal caries in bitewing radiographs using YOLOv8[J/OL]. Scientific Reports, 2025, 15(1): 4641. https://pubmed.ncbi.nlm.nih.gov/39920198/. DOI: 10.1038/s41598-024-84737-x.

[21] Zhu H, Cao Z, Lian L, et al. CariesNet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic X-ray image[J/OL]. Neural Computing and Applications, 2023, 35(22): 16051-16059. https://pubmed.ncbi.nlm.nih.gov/35017793/. DOI: 10.1007/s00521-021-06684-2.

[22] Huang F, Yin J, Ma Y, et al. Precise dental caries segmentation in X-rays with an attention and edge dual-decoder network[J/OL]. Medical & Biological Engineering & Computing, 2025, 63(8): 2259-2276. https://pubmed.ncbi.nlm.nih.gov/39961911/. DOI: 10.1007/s11517-025-03318-w.

[23] Hou S, Zhou T, Liu Y, et al. Teeth U-Net: A segmentation model of dental panoramic X-ray images for context semantics and contrast enhancement[J/OL]. Computers in Biology and Medicine, 2023, 152: 106296. https://pubmed.ncbi.nlm.nih.gov/36462370/. DOI: 10.1016/j.compbiomed.2022.106296.

[24] Ahmed WM, Azhari AA, Alnakhli R, et al. Development and evaluation of an artificial intelligence (AI) model for detecting dental caries from 3D intraoral scans[J/OL]. The Journal of Prosthetic Dentistry, 2025, 134(4): 1115.e1-1115.e8. https://pubmed.ncbi.nlm.nih.gov/40664593/. DOI: 10.1016/j.prosdent.2025.06.020.

[25] Zeng Z, Ramesh A, Ruan J, et al. Use of artificial intelligence to detect dental caries on intraoral photos[J/OL]. Quintessence International, 2025, 56(1): 82-91. https://pubmed.ncbi.nlm.nih.gov/39601186/. DOI: 10.3290/j.qi.b5857664.

[26] Sohrabi A, Ameli N, Mirimoghaddam M, et al. Deep learning-based assessment of pulp involvement in primary molars using YOLO v8[J/OL]. PLOS Digital Health, 2025, 4(4): e0000816. https://pubmed.ncbi.nlm.nih.gov/40198622/. DOI: 10.1371/journal.pdig.0000816.

[27] Jones B, Lambach M, Chen T, et al. Dental caries detection in children using intraoral scans and deep learning[J/OL]. Journal of Dentistry, 2025, 160: 105906. https://pubmed.ncbi.nlm.nih.gov/40527440/. DOI: 10.1016/j.jdent.2025.105906.

[28] Li S, Guo Y, Pang Z, et al. Automatic Dental Plaque Segmentation Based on Local-to-Global Features Fused Self-Attention Network[J/OL]. IEEE Journal of Biomedical and Health Informatics, 2022, 26(5): 2240-2251. https://pubmed.ncbi.nlm.nih.gov/35015655/. DOI: 10.1109/JBHI.2022.3141773.

[29] Shi J, Sun B, Ye X, et al. Semantic Decomposition Network With Contrastive and Structural Constraints for Dental Plaque Segmentation[J/OL]. IEEE Transactions on Medical Imaging, 2023, 42(4): 935-946. https://pubmed.ncbi.nlm.nih.gov/36367911/. DOI: 10.1109/TMI.2022.3221529.

[30] Ammar N, Kühnisch J . Diagnostic performance of artificial intelligence-aided caries detection on bitewing radiographs: a systematic review and meta-analysis[J/OL]. Japanese Dental Science Review, 2024, 60: 128-136. https://pubmed.ncbi.nlm.nih.gov/38450159/. DOI: 10.1016/j.jdsr.2024.02.001.

[31] Arsiwala-Scheppach LT, Castner NJ, Rohrer C, et al. Impact of artificial intelligence on dentists' gaze during caries detection: A randomized controlled trial[J/OL]. Journal of Dentistry, 2024, 140: 104793. https://pubmed.ncbi.nlm.nih.gov/38016620/. DOI: 10.1016/j.jdent.2023.104793.

[32] Szabó V, Szabó BT, Orhan K, et al. Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs[J/OL]. Journal of Dentistry, 2024, 147: 105105. https://pubmed.ncbi.nlm.nih.gov/38821394/. DOI: 10.1016/j.jdent.2024.105105.

[33] Dhanak N, Chougule VT, Nalluri K, et al. Artificial intelligence enabled smart phone app for real-time caries detection on bitewing radiographs[J/OL]. Bioinformation, 2024, 20(3): 243-247. https://pubmed.ncbi.nlm.nih.gov/38711998/. DOI: 10.6026/973206300200243.

[34] Santos‐Junior AO, Fontenele RC, Neves FS, et al. A novel artificial intelligence‐powered tool for automated root canal segmentation in single‐rooted teeth on cone‐beam computed tomography[J/OL]. International Endodontic Journal, 2025, 58(4): 658-671. https://pubmed.ncbi.nlm.nih.gov/39873266/. DOI: 10.1111/iej.14200.

[35] Slim ML, Jacobs R, de Souza Leal RM, et al. AI-driven segmentation of the pulp cavity system in mandibular molars on CBCT images using convolutional neural networks[J/OL]. Clinical Oral Investigations, 2024, 28(12): 650. https://pubmed.ncbi.nlm.nih.gov/39570431/. DOI: 10.1007/s00784-024-06009-2.

[36] Wang Y, Xia W, Yan Z, et al. Root canal treatment planning by automatic tooth and root canal segmentation in dental CBCT with deep multi-task feature learning[J/OL]. Medical Image Analysis, 2023, 85: 102750. https://pubmed.ncbi.nlm.nih.gov/36682153/. DOI: 10.1016/j.media.2023.102750.

[37] Jin L, Zhou W, Tang Y, et al. Detection of C-shaped mandibular second molars on panoramic radiographs using deep convolutional neural networks[J/OL]. Clinical Oral Investigations, 2024, 28(12): 646. https://pubmed.ncbi.nlm.nih.gov/39557710/. DOI: 10.1007/s00784-024-06049-8.

[38] Wu W, Chen S, Chen P, et al. Identification of Root Canal Morphology in Fused-rooted Mandibular Second Molars From X-ray Images Based on Deep Learning[J/OL]. Journal of Endodontics, 2024, 50(9): 1289-1297.e1. https://pubmed.ncbi.nlm.nih.gov/38821263/. DOI: 10.1016/j.joen.2024.05.014.

[39] Saghiri MA, Garcia-Godoy F, Gutmann JL, et al. The Reliability of Artificial Neural Network in Locating Minor Apical Foramen: A Cadaver Study[J/OL]. Journal of Endodontics, 2012, 38(8): 1130-1134. https://pubmed.ncbi.nlm.nih.gov/22794221/. DOI: 10.1016/j.joen.2012.05.004.

[40] Saghiri MA, Asgar K, Boukani KK, et al. A new approach for locating the minor apical foramen using an artificial neural network[J/OL]. International Endodontic Journal, 2012, 45(3): 257-265. https://pubmed.ncbi.nlm.nih.gov/22007705/. DOI: 10.1111/j.1365-2591.2011.01970.x.

[41] Qiao X, Zhang Z, Chen X . Multifrequency Impedance Method Based on Neural Network for Root Canal Length Measurement[J/OL]. Applied Sciences, 2020, 10(21): 7430. https://www.mdpi.com/2076-3417/10/21/7430. DOI: 10.3390/app10217430.

[42] Duman S, Yılmaz EF, Eşer G, et al. Detecting the presence of taurodont teeth on panoramic radiographs using a deep learning-based convolutional neural network algorithm[J/OL]. Oral Radiology, 2023, 39(1): 207-214. https://pubmed.ncbi.nlm.nih.gov/35612677/. DOI: 10.1007/s11282-022-00622-1.

[43] Abdelazim R, Fouad EM . Artificial intelligent-driven decision-making for automating root fracture detection in periapical radiographs[J/OL]. BDJ Open, 2024, 10(1): 76. https://pubmed.ncbi.nlm.nih.gov/39353905/. DOI: 10.1038/s41405-024-00260-1.

[44] Hu Z, Cao D, Hu Y, et al. Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images [J/OL]. BMC Oral Health, 2022, 22(1): 382. https://pubmed.ncbi.nlm.nih.gov/36064682/. DOI: 10.1186/s12903-022-02422-9.

[45] Yasin ET, Erturk M, Tassoker M, et al. Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction[J/OL]. Clinical Oral Investigations, 2025, 29(4): 203. https://pubmed.ncbi.nlm.nih.gov/40128451/. DOI: 10.1007/s00784-025-06285-6.

[46] Hu Y, Liu C, Liu W, et al. Fully automated method for three-dimensional segmentation and fine classification of mixed dentition in cone-beam computed tomography using deep learning[J/OL]. Journal of Dentistry, 2024, 151: 105398. https://pubmed.ncbi.nlm.nih.gov/39447958/. DOI: 10.1016/j.jdent.2024.105398.

[47] Choi E, Pang K, Jeong E, et al. Artificial intelligence in diagnosing dens evaginatus on periapical radiography with limited data availability[J/OL]. Scientific Reports, 2023, 13(1): 13232. https://pubmed.ncbi.nlm.nih.gov/37580409/. DOI: 10.1038/s41598-023-40472-3.

[48] Giraldo‐Roldán D, Araújo ALD, Moraes MC, et al. Artificial intelligence and radiomics in the diagnosis of intraosseous lesions of the gnathic bones: systematic review[J/OL]. Journal of Oral Pathology & Medicine, 2024, 53(7): 415-433. https://pubmed.ncbi.nlm.nih.gov/38807455/. DOI: 10.1111/jop.13548.

[49] Cheung K, Cheung W, Liu Y, et al. Establishment of a 3D esthetic analysis workflow on 3D virtual patient and preliminary evaluation[J/OL]. BMC Oral Health, 2024, 24(1): 328. https://pubmed.ncbi.nlm.nih.gov/38475773/. DOI: 10.1186/s12903-024-04085-0.

[50] Gong Z, Cai G, Zeng J, et al. Expertise‐inspired artificial intelligence pipeline for clinically applicable reconstruction of tooth‐centric radial planes: Development and multicenter validation[J/OL]. BMEMat, 2025, 3(3): e70010. https://doi.org/10.1002/bmm2.70010. DOI: 10.1002/bmm2.70010.

[51] Lee CT, Kabir T, Nelson J, et al. Use of the deep learning approach to measure alveolar bone level[J/OL]. Journal of Clinical Periodontology, 2022, 49(3): 260-269. https://pubmed.ncbi.nlm.nih.gov/34879437/. DOI: 10.1111/jcpe.13574.

[52] Xue T, Chen L, Sun Q . Deep learning method to automatically diagnose periodontal bone loss and periodontitis stage in dental panoramic radiograph[J/OL]. Journal of Dentistry, 2024, 150: 105373. https://pubmed.ncbi.nlm.nih.gov/39332519/. DOI: 10.1016/j.jdent.2024.105373.

[53] Mei L, Deng K, Cui Z, et al. Clinical knowledge-guided hybrid classification network for automatic periodontal disease diagnosis in X-ray image[J/OL]. Medical Image Analysis, 2025, 99: 103376. https://pubmed.ncbi.nlm.nih.gov/39536402/. DOI: 10.1016/j.media.2024.103376.

[54] Tan M, Cui Z, Li Y, et al. PerioAI: A digital system for periodontal disease diagnosis from an intra-oral scan and cone-beam CT image[J/OL]. Cell Reports Medicine, 2025, 6(6): 102186. https://pubmed.ncbi.nlm.nih.gov/40532658/. DOI: 10.1016/j.xcrm.2025.102186.

[55] Wu J, Tsoi JKH . AI-powered digital probing improves periodontal disease diagnosis[J/OL]. Cell Reports Medicine, 2025, 6(6): 102185. https://pubmed.ncbi.nlm.nih.gov/40532662/. DOI: 10.1016/j.xcrm.2025.102185.

[56] Wu ZY, Guo W, Zhou W, et al. Abductive multi-instance multi-label learning for periodontal disease classification with prior domain knowledge[J/OL]. Medical Image Analysis, 2025, 101: 103452. https://pubmed.ncbi.nlm.nih.gov/39826436/. DOI: 10.1016/j.media.2024.103452.

[57] Song L, Liu J, Qian B, et al. A Deep Multi-Modal CNN for Multi-Instance Multi-Label Image Classification[J/OL]. IEEE Transactions on Image Processing, 2018, 27(12): 6025-6038. https://pubmed.ncbi.nlm.nih.gov/30106729/. DOI: 10.1109/TIP.2018.2864920.

[58] Hilmi A, Patel S, Mirza K, et al. Efficacy of imaging techniques for the diagnosis of apical periodontitis: A systematic review[J/OL]. International Endodontic Journal, 2023, 56(S3): 326-339. https://pubmed.ncbi.nlm.nih.gov/37067066/. DOI: 10.1111/iej.13921.

[59] Uraba S, Ebihara A, Komatsu K, et al. Ability of Cone-beam Computed Tomography to Detect Periapical Lesions That Were Not Detected by Periapical Radiography: A Retrospective Assessment According to Tooth Group[J/OL]. Journal of Endodontics, 2016, 42(8): 1186-1190. https://pubmed.ncbi.nlm.nih.gov/27372162/. DOI: 10.1016/j.joen.2016.04.026.

[60] Fu WT, Zhu QK, Li N et al. Clinically Oriented CBCT Periapical Lesion Evaluation via 3D CNN Algorithm[J/OL]. Journal of Dental Research, 2024, 103(1): 5-12. https://pubmed.ncbi.nlm.nih.gov/37968798/. DOI: 10.1177/00220345231201793.

[61] Oser DG, Henson BR, Shiang EY, et al. Incidental Findings in Small Field of View Cone-beam Computed Tomography Scans[J/OL]. Journal of Endodontics, 2017, 43(6): 901-904. https://pubmed.ncbi.nlm.nih.gov/28359665/. DOI: 10.1016/j.joen.2017.01.033.

[62] Chau KK, Zhu M, AlHadidi A, et al. A novel AI model for detecting periapical lesion on CBCT: CBCT-SAM[J/OL]. Journal of Dentistry, 2025, 153: 105526. https://pubmed.ncbi.nlm.nih.gov/39667487/. DOI: 10.1016/j.jdent.2024.105526.

[63] Yilmaz E, Kayikcioglu T, Kayipmaz S . Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography[J/OL]. Computer Methods and Programs in Biomedicine, 2017, 146: 91-100. https://pubmed.ncbi.nlm.nih.gov/28688493/. DOI: 10.1016/j.cmpb.2017.05.012.

[64] Lu C, Lewis JS, Dupont WD, et al. An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival[J/OL]. Modern Pathology, 2017, 30(12): 1655-1665. https://pubmed.ncbi.nlm.nih.gov/28776575/. DOI: 10.1038/modpathol.2017.98.

[65] Rokhshad R, Salehi SN, Yavari A, et al. Deep learning for diagnosis of head and neck cancers through radiographic data: a systematic review and meta-analysis[J/OL]. Oral Radiology, 2024, 40(1): 1-20. https://pubmed.ncbi.nlm.nih.gov/37855976/. DOI: 10.1007/s11282-023-00715-5.

[66] Mascharak S, Baird BJ, Holsinger FC . Detecting oropharyngeal carcinoma using multispectral, narrow‐band imaging and machine learning[J/OL]. The Laryngoscope, 2018, 128(11): 2514-2520. https://pubmed.ncbi.nlm.nih.gov/29577322/. DOI: 10.1002/lary.27159.

[67] Fouad S, Randell D, Galton A, et al. Unsupervised morphological segmentation of tissue compartments in histopathological images[J/OL]. PLOS ONE, 2017, 12(11): e0188717. https://pubmed.ncbi.nlm.nih.gov/29190786/. DOI: 10.1371/journal.pone.0188717.

[68] Zhang L, Wu Y, Zheng B, et al. Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated Raman scattering microscopy[J/OL]. Theranostics, 2019, 9(9): 2541-2554. https://pubmed.ncbi.nlm.nih.gov/31131052/. DOI: 10.7150/thno.32655.

[69] Bae S, Choi YS, Sohn B, et al. Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach[J/OL]. Yonsei Medical Journal, 2020, 61(10): 895. https://pubmed.ncbi.nlm.nih.gov/32975065/. DOI: 10.3349/ymj.2020.61.10.895.

[70] Wu Y, Zou S, Lv P, et al. Accuracy of an autonomous dental implant robotic system in dental implant surgery[J/OL]. The Journal of Prosthetic Dentistry, 2025, 133(3): 764-770. https://pubmed.ncbi.nlm.nih.gov/39142994/. DOI: 10.1016/j.prosdent.2024.07.020.

[71] Elgarba BM, Fontenele RC, Tarce M, et al. Artificial intelligence serving pre-surgical digital implant planning: A scoping review[J/OL]. Journal of Dentistry, 2024, 143: 104862. https://pubmed.ncbi.nlm.nih.gov/38336018/. DOI: 10.1016/j.jdent.2024.104862.

[72] Chen J, Ding Y, Cao R, et al. Accuracy of a Novel Robot‐Assisted System and Dynamic Navigation System for Dental Implant Placement: A Clinical Retrospective Study[J/OL]. Clinical Oral Implants Research, 2025, 36(6): 725-735. https://pubmed.ncbi.nlm.nih.gov/39953801/. DOI: 10.1111/clr.14420.

[73] Proshchenko A, Terekhov S, Vesova O, et al. Utilization of artificial intelligence for predictive modeling in dental implantology[J/OL]. Georgian Medical News, 2024, (350): 6-15. https://pubmed.ncbi.nlm.nih.gov/39089263/.

[74] Hendrickx J, Gracea RS, Vanheers M, et al. Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis[J/OL]. European Journal of Orthodontics, 2024, 46(4): cjae029. https://pubmed.ncbi.nlm.nih.gov/38895901/. DOI: 10.1093/ejo/cjae029.

[75] Jiang F, Guo Y, Yang C, et al. Artificial intelligence system for automated landmark localization and analysis of cephalometry[J/OL]. Dentomaxillofacial Radiology, 2023, 52(1): 20220081. https://pubmed.ncbi.nlm.nih.gov/36279185/. DOI: 10.1259/dmfr.20220081.

[76] Olawade DB, Leena N, Egbon E, et al. AI-Driven Advancements in Orthodontics for Precision and Patient Outcomes[J/OL]. Dentistry Journal, 2025, 13(5): 198. https://pubmed.ncbi.nlm.nih.gov/40422618/. DOI: 10.3390/dj13050198.

[77] El-Angbawi A, McIntyre G, Fleming PS, et al. Non-surgical adjunctive interventions for accelerating tooth movement in patients undergoing orthodontic treatment[J/OL]. Cochrane Database of Systematic Reviews, 2023, 6(6): CD010887. https://pubmed.ncbi.nlm.nih.gov/37339352/. DOI: 10.1002/14651858.CD010887.pub3.

[78] Kovalainen T, Pramila-Savukoski S, Kuivila HM, et al. Utilising artificial intelligence in developing education of health sciences higher education: An umbrella review of reviews[J/OL]. Nurse Education Today, 2025, 147: 106600. https://pubmed.ncbi.nlm.nih.gov/39904286/. DOI: 10.1016/j.nedt.2025.106600.

[79] Cheng C, Yinan X, Zongxin X, et al. Robotic and Microrobotic Tools for Dental Therapy[J/OL]. Journal of Healthcare Engineering, 2022, 2022: 1-12. https://pubmed.ncbi.nlm.nih.gov/35222881/. DOI: 10.1155/2022/3265462.

[80] Shao L, Li X, Fu T, et al. Robot‐assisted augmented reality surgical navigation based on optical tracking for mandibular reconstruction surgery[J/OL]. Medical Physics, 2024, 51(1): 363-377. https://pubmed.ncbi.nlm.nih.gov/37431603/. DOI: 10.1002/mp.16598.

[81] Li Y, Inamochi Y, Wang Z, et al. Clinical application of robots in dentistry: A scoping review[J/OL]. Journal of Prosthodontic Research, 2024, 68(2): 193-205. https://pubmed.ncbi.nlm.nih.gov/37302842/. DOI: 10.2186/jpr.JPR_D_23_00027.

[82] Adel S, Zaher A, El Harouni N, et al. Robotic Applications in Orthodontics: Changing the Face of Contemporary Clinical Care[J/OL]. BioMed Research International, 2021, 2021(1): 9954615. https://pubmed.ncbi.nlm.nih.gov/34222490/. DOI: 10.1155/2021/9954615.

[83] Wang M, Liu F, Zhao X, et al. Robot-assisted surgery for dental implant placement: A narrative review[J/OL]. Journal of Dentistry, 2024, 146: 105034. https://pubmed.ncbi.nlm.nih.gov/38729287/. DOI: 10.1016/j.jdent.2024.105034.

[84] Kang SW, Lee SH, Ryu HR, et al. Initial experience with robot-assisted modified radical neck dissection for the management of thyroid carcinoma with lateral neck node metastasis[J/OL]. Surgery, 2010, 148(6): 1214-1221. https://pubmed.ncbi.nlm.nih.gov/21134554/. DOI: 10.1016/j.surg.2010.09.016.

[85] De Ceulaer J, De Clercq C, Swennen G . Robotic surgery in oral and maxillofacial, craniofacial and head and neck surgery: A systematic review of the literature[J/OL]. International Journal of Oral and Maxillofacial Surgery, 2012, 41(11): 1311-1324. https://pubmed.ncbi.nlm.nih.gov/22910368/. DOI: 10.1016/j.ijom.2012.05.035.

[86] On SW, Cho SW, Park SY, et al. Advancements in computer-assisted orthognathic surgery: A comprehensive review and clinical application in South Korea[J/OL]. Journal of Dentistry, 2024, 146: 105061. https://pubmed.ncbi.nlm.nih.gov/38729289/. DOI: 10.1016/j.jdent.2024.105061.

[87] Khan K, Dobbs T, Swan MC, et al. Trans-oral robotic cleft surgery (TORCS) for palate and posterior pharyngeal wall reconstruction: A feasibility study[J/OL]. Journal of Plastic, Reconstructive & Aesthetic Surgery, 2016, 69(1): 97-100. https://pubmed.ncbi.nlm.nih.gov/26409954/. DOI: 10.1016/j.bjps.2015.08.020.

[88] Mah J, Sachdeva R . Computer-assisted orthodontic treatment: The SureSmile process[J/OL]. American Journal of Orthodontics and Dentofacial Orthopedics, 2001, 120(1): 85-87. https://pubmed.ncbi.nlm.nih.gov/11455383/. DOI: 10.1067/mod.2001.117686.

[89] Liu L, Watanabe M, Ichikawa T . Robotics in Dentistry: A Narrative Review[J/OL]. Dentistry Journal, 2023, 11(3): 62. https://pubmed.ncbi.nlm.nih.gov/36975559/. DOI: 10.3390/dj11030062.

[90] Wang D, Wang L, Zhang Y, et al. Preliminary study on a miniature laser manipulation robotic device for tooth crown preparation[J/OL]. The International Journal of Medical Robotics and Computer Assisted Surgery, 2014, 10(4): 482-494. https://pubmed.ncbi.nlm.nih.gov/24390978/. DOI: 10.1002/rcs.1560.

[91] Yuan F, Wang Y, Zhang Y, et al. An automatic tooth preparation technique: A preliminary study[J/OL]. Scientific Reports, 2016, 6(1): 25281. https://pubmed.ncbi.nlm.nih.gov/27125874/. DOI: 10.1038/srep25281.

[92] Ariji Y, Nakayama M, Nishiyama W, et al. Potential clinical application of masseter and temporal muscle massage treatment using an oral rehabilitation robot in temporomandibular disorder patients with myofascial pain[J/OL]. Cranio: the journal of craniomandibular practice, 2015, 33(4): 256-262. https://pubmed.ncbi.nlm.nih.gov/26714800/. DOI: 10.1080/08869634.2015.1097303.

[93] Babeer A, Bukhari S, Alrehaili R, et al. Microrobotics in endodontics: A perspective[J/OL]. International Endodontic Journal, 2024, 57(7): 861-871. https://pubmed.ncbi.nlm.nih.gov/38761098/. DOI: 10.1111/iej.14082.

[94] Vanderveken OM, Dieltjens M, Wouters K, et al. Objective measurement of compliance during oral appliance therapy for sleep-disordered breathing[J/OL]. Thorax, 2013, 68(1): 91-96. https://pubmed.ncbi.nlm.nih.gov/22993169/. DOI: 10.1136/thoraxjnl-2012-201900.

[95] Oyonarte R, Zárate M, Rodriguez F . Low-Intensity Pulsed Ultrasound Stimulation of Condylar Growth in Rats[J/OL]. The Angle Orthodontist, 2009, 79(5): 964-970. https://pubmed.ncbi.nlm.nih.gov/19705932/. DOI: 10.2319/080708-414.1.

[96] Gjerde K, Lehmann S, Naterstad IF, et al. Reliability of an adherence monitoring sensor embedded in an oral appliance used for treatment of obstructive sleep apnoea[J/OL]. Journal of Oral Rehabilitation, 2018, 45(2): 110-115. https://pubmed.ncbi.nlm.nih.gov/29090478/. DOI: 10.1111/joor.12584.

[97] Babeer A, Oh M, Ren Z, et al. Microrobotics for Precision Biofilm Diagnostics and Treatment[J/OL]. Journal of Dental Research, 2022, 101(9): 1009-1014. https://pubmed.ncbi.nlm.nih.gov/35450484/. DOI: 10.1177/00220345221087149.

[98] Tran HH, Watkins A, Oh MJ, et al. Targeting biofilm infections in humans using small scale robotics[J/OL]. Trends in Biotechnology, 2024, 42(4): 479-495. https://pubmed.ncbi.nlm.nih.gov/37968157/. DOI: 10.1016/j.tibtech.2023.10.004.

[99] Oh MJ, Babeer A, Liu Y, et al. Surface Topography-Adaptive Robotic Superstructures for Biofilm Removal and Pathogen Detection on Human Teeth[J/OL]. ACS Nano, 2022, 16(8): 11998-12012. https://pubmed.ncbi.nlm.nih.gov/35764312/. DOI: 10.1021/acsnano.2c01950.

[100] Jaruchotiratanasakul N, Tran HH, Steager EB, et al. Artificial intelligence in endodontics/robotics-microrobotics in endodontics: A revolutionary approach to root canal treatment and nanozymes[J/OL]. Dental Clinics of North America, 2025, 69(4): 515-526. https://pubmed.ncbi.nlm.nih.gov/41106907/. DOI: 10.1016/j.cden.2025.05.002.

[101] Li Y, Zhang B, Li D, et al. Machine Learning and Mendelian Randomization Reveal Molecular Mechanisms and Causal Relationships of Immune‐Related Biomarkers in Periodontitis[J/OL]. Mediators of Inflammation, 2024, 2024(1): 9983323. https://pubmed.ncbi.nlm.nih.gov/39717623/. DOI: 10.1155/mi/9983323.

[102] Zhao A, Chen Y, Yang H, et al. Exploring the risk factors and clustering patterns of periodontitis in patients with different subtypes of diabetes through machine learning and cluster analysis[J/OL]. Acta Odontologica Scandinavica, 2024, 83: 653-665. https://pubmed.ncbi.nlm.nih.gov/39632553/. DOI: 10.2340/aos.v83.42435.

[103] Suh B, Yu H, Cha JK, et al. Explainable Deep Learning Approaches for Risk Screening of Periodontitis[J/OL]. Journal of Dental Research, 2025, 104(1): 45-53. https://pubmed.ncbi.nlm.nih.gov/39563207/. DOI: 10.1177/00220345241286488.

[104] Xiong D, Marcus M, Maida CA, et al. Development of short forms for screening children's dental caries and urgent treatment needs using item response theory and machine learning methods[J/OL]. PLOS ONE, 2024, 19(3): e0299947. https://pubmed.ncbi.nlm.nih.gov/38517846/. DOI: 10.1371/journal.pone.0299947.

[105] Toledo Reyes L, Knorst J, Ortiz F, et al. Early Childhood Predictors for Dental Caries: A Machine Learning Approach[J/OL]. Journal of Dental Research, 2023, 102(9): 999-1006. https://pubmed.ncbi.nlm.nih.gov/37246832/. DOI: 10.1177/00220345231170535.

[106] Qu X, Zhang C, Houser SH, et al. Prediction model for early childhood caries risk based on behavioral determinants using a machine learning algorithm[J/OL]. Computer Methods and Programs in Biomedicine, 2022, 227: 107221. https://pubmed.ncbi.nlm.nih.gov/36384058/. DOI: 10.1016/j.cmpb.2022.107221.

[107] Xie C, Li Y, Liu K, et al. A hybrid unsupervised clustering method for predicting the risk of dental implant loss[J/OL]. Journal of Dentistry, 2024, 149: 105260. https://pubmed.ncbi.nlm.nih.gov/39096996/. DOI: 10.1016/j.jdent.2024.105260.

[108] Bonde A, Varadarajan KM, Bonde N, et al. Assessing the utility of deep neural networks in predicting postoperative surgical complications: a retrospective study[J/OL]. The Lancet Digital Health, 2021, 3(8): e471-e485. https://pubmed.ncbi.nlm.nih.gov/34215564/. DOI: 10.1016/S2589-7500(21)00084-4.

[109] Gao X, Xin X, Li Z, et al. Predicting postoperative pain following root canal treatment by using artificial neural network evaluation[J/OL]. Scientific Reports, 2021, 11(1): 17243. https://pubmed.ncbi.nlm.nih.gov/34446767/. DOI: 10.1038/s41598-021-96777-8.

[110] Li Y, Zeng G, Zhang Y, et al. AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapy[J/OL]. IEEE Journal of Biomedical and Health Informatics, 2022, 26(4): 1684-1695. https://pubmed.ncbi.nlm.nih.gov/34797767/. DOI: 10.1109/JBHI.2021.3129245.

[111] Qu Y, Lin Z, Yang Z, et al. Machine learning models for prognosis prediction in endodontic microsurgery[J/OL]. Journal of Dentistry, 2022, 118: 103947. https://pubmed.ncbi.nlm.nih.gov/35021070/. DOI: 10.1016/j.jdent.2022.103947.

[112] Qu Y, Wen Y, Chen M, et al. Predicting case difficulty in endodontic microsurgery using machine learning algorithms[J/OL]. Journal of Dentistry, 2023, 133: 104522. https://pubmed.ncbi.nlm.nih.gov/37080531/. DOI: 10.1016/j.jdent.2023.104522.

[113] Alam MK, Alanazi DSA, Alruwaili SRF, et al. Assessment of AI Models in Predicting Treatment Outcomes in Orthodontics[J/OL]. Journal of Pharmacy and Bioallied Sciences, 2024, 16(Suppl 1): S540-S542. https://pubmed.ncbi.nlm.nih.gov/38595553/. DOI: 10.4103/jpbs.jpbs_852_23.

[114] Park JA, Moon JH, Lee JM, et al. Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods?[J/OL]. The Angle Orthodontist, 2024, 94(5): 549-556. https://pubmed.ncbi.nlm.nih.gov/39230019/. DOI: 10.2319/111423-756.1.

[115] Lopes dos Santos R, Spinola MDS, Carvalho E, et al. Effectiveness of a New App in Improving Oral Hygiene in Orthodontic Patients: A Pilot Study[J/OL]. International Dental Journal, 2023, 73(3): 395-402. https://pubmed.ncbi.nlm.nih.gov/36241273/. DOI: 10.1016/j.identj.2022.08.010.

[116] Sangalli L, Savoldi F, Dalessandri D, et al. Effects of remote digital monitoring on oral hygiene of orthodontic patients: a prospective study[J/OL]. BMC Oral Health, 2021, 21(1): 435. https://pubmed.ncbi.nlm.nih.gov/34493255/. DOI: 10.1186/s12903-021-01793-9.

[117] Almarhoumi A . Accuracy of Artificial Intelligence in Predicting Facial Changes Post-Orthognathic Surgery: A Comprehensive Scoping Review[J/OL]. Journal of Clinical and Experimental Dentistry, 2024, 16(5): e624-e633. https://pubmed.ncbi.nlm.nih.gov/38988747/. DOI: 10.4317/jced.61500.

[118] Chen Y, Lee JKY, Kwong G, et al. Morphology and fracture behavior of lithium disilicate dental crowns designed by human and knowledge-based AI[J/OL]. Journal of the Mechanical Behavior of Biomedical Materials, 2022, 131: 105256. https://pubmed.ncbi.nlm.nih.gov/35508087/. DOI: 10.1016/j.jmbbm.2022.105256.

[119] Kong HJ, Kim YL . Application of artificial intelligence in dental crown prosthesis: a scoping review[J/OL]. BMC Oral Health, 2024, 24(1): 937. https://pubmed.ncbi.nlm.nih.gov/39138474/. DOI: 10.1186/s12903-024-04657-0.

[120] Almedaires M, Delgado A, Abdulhameed N, et al. Predicting Final Restoration Color Using Neural Network Models: The Impact of Substrate Lightness Versus Ceramic Shade, Translucency and Thickness[J/OL]. Journal of Esthetic and Restorative Dentistry, 2025, 37(7): 1900-1906. https://pubmed.ncbi.nlm.nih.gov/40151065/. DOI: 10.1111/jerd.13469.

[121] Yamaguchi S, Lee C, Karaer O, et al. Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI[J/OL]. Journal of Dental Research, 2019, 98(11): 1234-1238. https://pubmed.ncbi.nlm.nih.gov/31379234/. DOI: 10.1177/0022034519867641.

[122] Leitão CIMB, Fernandes GVDO, Azevedo LPP, et al. Clinical performance of monolithic CAD/CAM tooth-supported zirconia restorations: systematic review and meta-analysis[J/OL]. Journal of Prosthodontic Research, 2022, 66(3): 374-384. https://pubmed.ncbi.nlm.nih.gov/34615842/. DOI: 10.2186/jpr.JPR_D_21_00081.

[123] Schropp L, Sørensen APS, Devlin H, et al. Use of artificial intelligence software in dental education: A study on assisted proximal caries assessment in bitewing radiographs[J/OL]. European Journal of Dental Education, 2024, 28(2): 490-496. https://pubmed.ncbi.nlm.nih.gov/37961027/. DOI: 10.1111/eje.12973.

[124] Ayan E, Bayraktar Y, Çelik Ç, et al. Dental student application of artificial intelligence technology in detecting proximal caries lesions[J/OL]. Journal of Dental Education, 2024, 88(4): 490-500. https://pubmed.ncbi.nlm.nih.gov/38200405/. DOI: 10.1002/jdd.13437.

[125] Zheng L, Wang H, Mei L, et al. Artificial intelligence in digital cariology: a new tool for the diagnosis of deep caries and pulpitis using convolutional neural networks[J/OL]. Annals of Translational Medicine, 2021, 9(9): 763. https://pubmed.ncbi.nlm.nih.gov/34268376/. DOI: 10.21037/atm-21-119.

[126] Hattori A, Tonami KI, Tsuruta J, et al. Effect of the haptic 3D virtual reality dental training simulator on assessment of tooth preparation[J/OL]. Journal of Dental Sciences, 2022, 17(1): 514-520. https://doi.org/ 10.1016/j.jds.2021.06.022. https://pubmed.ncbi.nlm.nih.gov/35574075/. DOI: 10.1016/j.jds.2021.06.022.

[127] Patil S, Bhandi S, Awan KH, et al. Effectiveness of haptic feedback devices in preclinical training of dental students-a systematic review[J/OL]. BMC Oral Health, 2023, 23(1): 739. https://pubmed.ncbi.nlm.nih.gov/37817151/. DOI: 10.1186/s12903-023-03410-3.

[128] Razavi M, Talebi HA, Zareinejad M, et al. A GPU‐implemented physics‐based haptic simulator of tooth drilling[J/OL]. The International Journal of Medical Robotics and Computer Assisted Surgery, 2015, 11(4): 476-485. https://pubmed.ncbi.nlm.nih.gov/25582358/. DOI: 10.1002/rcs.1635.

[129] Vannaprathip N, Haddawy P, Schultheis H, et al. SDMentor: A virtual reality-based intelligent tutoring system for surgical decision making in dentistry[J/OL]. Artificial Intelligence in Medicine, 2025, 162: 103092. https://pubmed.ncbi.nlm.nih.gov/40015210/. DOI: 10.1016/j.artmed.2025.103092.

[130] A dental training robot[J/OL]. The Journal of the Kansas State Dental Association, 1969, 53(4): 161. https://pubmed.ncbi.nlm.nih.gov/5260417/.

[131] Tanzawa T, Futaki K, Tani C, et al. Introduction of a robot patient into dental education[J/OL]. European Journal of Dental Education, 2012, 16(1): e195-199. https://pubmed.ncbi.nlm.nih.gov/22251346/. DOI: 10.1111/j.1600-0579.2011.00697.x.

[132] Yu K, Matsunaga T, Kawana H, et al. Frequency-Based Analysis of the Relationship between Cutting Force and CT Number for an Implant-Surgery-Teaching Robot[J/OL]. IEEJ Journal of Industry Applications, 2017, 6(1): 66-72. https://www.jstage.jst.go.jp/article/ieejjia/6/1/6_66/_article/. DOI: 10.1541/ieejjia.6.66.

[133] Yilmaz BE, Gokkurt Yilmaz BN, Ozbey F . Artificial intelligence performance in answering multiple-choice oral pathology questions: a comparative analysis[J/OL]. BMC Oral Health, 2025, 25(1): 573. https://pubmed.ncbi.nlm.nih.gov/40234873/. DOI: 10.1186/s12903-025-05926-2.

[134] Sabri H, Saleh MHA, Hazrati P, et al. Performance of three artificial intelligence (AI)‐based large language models in standardized testing; implications for AI‐assisted dental education[J/OL]. Journal of Periodontal Research, 2025, 60(2): 121-133. https://pubmed.ncbi.nlm.nih.gov/39030766/. DOI: 10.1111/jre.13323.

[135] Danesh A, Pazouki H, Danesh F, et al. Artificial intelligence in dental education: ChatGPT's performance on the periodontic in‐service examination[J/OL]. Journal of Periodontology, 2024, 95(7): 682-687. https://pubmed.ncbi.nlm.nih.gov/38197146/. DOI: 10.1002/JPER.23-0514.

[136] Jalali P, Mohammad-Rahimi H, Wang FM, et al. Performance of 7 Artificial Intelligence Chatbots on Board-style Endodontic Questions[J/OL]. Journal of Endodontics, 2025, 51(10): 1413-1419. https://pubmed.ncbi.nlm.nih.gov/40581328/. DOI: 10.1016/j.joen.2025.06.014.

[137] Abdulrab S, Abada H, Mashyakhy M, et al. Performance of 4 Artificial Intelligence Chatbots in Answering Endodontic Questions[J/OL]. Journal of Endodontics, 2025, 51(5): 602-608. https://pubmed.ncbi.nlm.nih.gov/39814135/. DOI: 10.1016/j.joen.2025.01.002.

[138] Huang C, Chen C, Chutinan S . Integrating Generative AI in Case‐Based Collaborative Learning: Student's Perceptions[J/OL]. Journal of Dental Education, 2025, 89(S3): 1844-1847. https://pubmed.ncbi.nlm.nih.gov/39923191/. DOI: 10.1002/jdd.13846.

[139] Baxmann M, Kárpáti K, Baráth Z . The potentials and challenges of integrating generative artificial intelligence (AI) in dental and orthodontic education: a systematic review[J/OL]. BMC Oral Health, 2025, 25(1): 905. https://pubmed.ncbi.nlm.nih.gov/40462083/. DOI: 10.1186/s12903-025-06070-7.

[140] Metin U, Goymen M . Information from digital and human sources: A comparison of chatbot and clinician responses to orthodontic questions[J/OL]. American Journal of Orthodontics and Dentofacial Orthopedics, 2025, 168(3): 348-357. https://pubmed.ncbi.nlm.nih.gov/40327024/. DOI: 10.1016/j.ajodo.2025.04.008.

[141] Islam S. Evaluating the impact of AI-generated educational content on patient understanding and anxiety in endodontics and restorative dentistry: a comparative study[J/OL]. BMC Oral Health, 2025, 25(1): 689. https://pubmed.ncbi.nlm.nih.gov/40335999/. DOI: 10.1186/s12903-025-06069-0.

[142] Rodrigues J, Dos Santos P, Garcia P, et al. Evaluation of motivation methods used to obtain appropriate oral hygiene levels in schoolchildren[J/OL]. International Journal of Dental Hygiene, 2003, 1(4): 227-232. https://pubmed.ncbi.nlm.nih.gov/16451505/. DOI: 10.1034/j.1601-5037.2003.00038.x.

[143] Rodrigues JA, dos Santos PA, Baseggio W, et al. Oral Hygiene Indirect Instruction and Periodic Reinforcements: Effects on Index Plaque in Schoolchildren[J/OL]. Journal of Clinical Pediatric Dentistry, 2009, 34(1): 31-34. https://pubmed.ncbi.nlm.nih.gov/19953806/. DOI: 10.17796/jcpd.34.1.n426k2862m42r67n.

[144] Ahire M, Dani N, Muttha R . Dental health education through the brushing ROBOTUTOR: A new learning experience[J/OL]. Journal of Indian Society of Periodontology, 2012, 16(3): 417-420. https://pubmed.ncbi.nlm.nih.gov/23162339/. DOI: 10.4103/0972-124X.100922.

[145] Pan XY, Bi XY, Nong YN, et al. The efficacy of socially assistive robots in improving children's pain and negative affectivity during needle-based invasive treatment: A systematic review and meta-analysis[J/OL]. BMC Pediatrics, 2024, 24(1): 643. https://pubmed.ncbi.nlm.nih.gov/39390439/. DOI: 10.1186/s12887-024-05116-z.

[146] Kasimoglu Y, Kocaaydin S, Karsli E, et al. Robotic approach to the reduction of dental anxiety in children[J/OL]. Acta Odontologica Scandinavica, 2020, 78(6): 474-480. https://pubmed.ncbi.nlm.nih.gov/32730719/. DOI: 10.1080/00016357.2020.1800084.

[147] Santos PS, do Nascimento LP, Martorell LB, et al. Dental education and undue exposure of patients' image in social media: A literature review[J/OL]. European Journal of Dental Education, 2021, 25(3): 556-572. https://pubmed.ncbi.nlm.nih.gov/33220149/. DOI: 10.1111/eje.12633.

[148] Kosan E, Krois J, Wingenfeld K, et al. Patients' Perspectives on Artificial Intelligence in Dentistry: A Controlled Study[J/OL]. Journal of Clinical Medicine, 2022, 11(8): 2143. https://pubmed.ncbi.nlm.nih.gov/35456236/. DOI: 10.3390/jcm11082143.

[149] Ibrahim M, Omidi M, Guentsch A, et al. Ensuring integrity in dental education: Developing a novel model for consistent and traceable image analysis in preclinical endodontic procedures[J/OL]. International Endodontic Journal, 2026, 59(6): 1122-1131. https://pubmed.ncbi.nlm.nih.gov/40536276/. DOI: 10.1111/iej.14273.

[150] Hsieh HC, Chen CY, Chou CH, et al. Deep learning-based automatic image classification of oral cancer cells acquiring chemoresistance in vitro [J/OL]. PLOS ONE, 2024, 19(11): e0310304. https://pubmed.ncbi.nlm.nih.gov/39485749/. DOI: 10.1371/journal.pone.0310304.

[151] Trindade F, Perpétuo L, Ferreira R, et al. Automatic text-mining as an unbiased approach to uncover molecular associations between periodontitis and coronary artery disease[J/OL]. Biomarkers, 2021, 26(5): 385-394. https://pubmed.ncbi.nlm.nih.gov/33736543/. DOI: 10.1080/1354750X.2021.1904002.

[152] Luo L, Zheng W, Chen C, et al. Searching for essential genes and drug discovery in breast cancer and periodontitis via text mining and bioinformatics analysis[J/OL]. Anti-Cancer Drugs, 2021, 32(10): 1038-1045. https://pubmed.ncbi.nlm.nih.gov/34183495/. DOI: 10.1097/CAD.0000000000001108.

[153] Yadalam PK, Ayyachamy S, Barbosa FT, et al. Kolmogorov-Arnold networks for predicting drug-gene associations of HDAC1 inhibitors in periodontitis[J/OL]. Computational Biology and Chemistry, 2025, 118: 108451. https://pubmed.ncbi.nlm.nih.gov/40233475/. DOI: 10.1016/j.compbiolchem.2025.108451.

[154] Sharma V, Singh A, Chauhan S, et al. Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer[J/OL]. Current Drug Delivery, 2024, 21(6): 870-886. https://pubmed.ncbi.nlm.nih.gov/37670704/. DOI: 10.2174/1567201821666230905090621.

[155] Hillary VE, Gandhi Gopalsamy R, Santana LAD, et al. The impact of generative artificial intelligence (AI) on the development of personalized pharmaceuticals and the future of precision medicine[J/OL]. EXCLI Journal, 2024, 23: 1459-1462. https://pubmed.ncbi.nlm.nih.gov/39790560/. DOI: 10.17179/excli2024-7915.

[156] Zhang Q, Jiao Y, Ma N, et al. Identification of Endoplasmic Reticulum Stress-Related Biomarkers of Periodontitis Based on Machine Learning: A Bioinformatics Analysis[J/OL]. Disease Markers, 2022, 2022: 8611755. https://pubmed.ncbi.nlm.nih.gov/36072904/. DOI: 10.1155/2022/8611755.

[157] Chen C, Xie Z, Yang S, et al. Machine Learning Approach to Investigating Macrophage Polarization on Various Titanium Surface Characteristics[J/OL]. BME Frontiers, 2025, 6: 0100. https://pubmed.ncbi.nlm.nih.gov/40012846/. DOI: 10.34133/bmef.0100.

[158] Shi C, Ou X, Lei X, et al. Myeloid dendritic cells and periodontal disease association: integrated study of single-cell sequencing and Mendelian randomization analysis[J/OL]. Frontiers in Immunology, 2025, 15: 1522281. https://pubmed.ncbi.nlm.nih.gov/39830509/. DOI: 10.3389/fimmu.2024.1522281.

[159] Shi C, Ou X, Huang L, et al. Integrating GEO Database, Mendelian Randomization, and Molecular Docking to Identify HLA‐C as a Potential Therapeutic Target for Periodontitis[J/OL]. Mediators of Inflammation, 2025, 2025(1): 9163431. https://pubmed.ncbi.nlm.nih.gov/40692979/. DOI: 10.1155/mi/9163431.

[160] Sun S, Ren J, Zeng X, et al. Integrated machine learning and single-cell RNA sequencing reveal COL4A2 and CXCL6 as oxidative stress-associated biomarkers in periodontitis[J/OL]. Frontiers in Immunology, 2025, 16: 1598642. https://pubmed.ncbi.nlm.nih.gov/40539067/. DOI: 10.3389/fimmu.2025.1598642.

[161] DeMeo B, Nesbitt C, Miller SA, et al. Active learning framework leveraging transcriptomics identifies modulators of disease phenotypes[J/OL]. Science, 2025, 390(6776): eadi8577. https://pubmed.ncbi.nlm.nih.gov/41129612/. DOI: 10.1126/science.adi8577.

[162] Jabbour E, Kantarjian HM, Aldoss I, et al. Ponatinib vs Imatinib in Frontline Philadelphia Chromosome-Positive Acute Lymphoblastic Leukemia[J/OL]. JAMA, 2024, 331(21): 1814-1823. https://pubmed.ncbi.nlm.nih.gov/38722621/. DOI: 10.1001/jama.2024.4783.

[163] Nordling L. How ChatGPT is transforming the postdoc experience[J/OL]. Nature, 2023, 622(7983): 655-657. https://pubmed.ncbi.nlm.nih.gov/37845528/. DOI: 10.1038/d41586-023-03235-8.

[164] Hutson M. Could AI help you to write your next paper?[J/OL]. Nature, 2022, 611(7934): 192-193. https://pubmed.ncbi.nlm.nih.gov/36316468/. DOI: 10.1038/d41586-022-03479-w.

[165] Khraisha Q, Put S, Kappenberg J, et al. Can large language models replace humans in systematic reviews? Evaluating GPT‐4's efficacy in screening and extracting data from peer‐reviewed and grey literature in multiple languages[J/OL]. Research Synthesis Methods, 2024, 15(4): 616-626. https://pubmed.ncbi.nlm.nih.gov/38484744/. DOI: 10.1002/jrsm.1715.

[166] Zheng J, Pathak JL, Li S, et al. An optimized code-free AI approach for efficient and accurate literature screening in bone organoid research[J/OL]. Journal of Biomedical Informatics, 2025, 170: 104911. https://pubmed.ncbi.nlm.nih.gov/40935223/. DOI: 10.1016/j.jbi.2025.104911.

[167] Chen H, Jiang Z, Liu X, et al. Can large language models fully automate or partially assist paper selection in systematic reviews?[J/OL]. British Journal of Ophthalmology, 2025, 109(8): 962-966. https://pubmed.ncbi.nlm.nih.gov/39814458/. DOI: 10.1136/bjo-2024-326254.

[168] Tools such as ChatGPT threaten transparent science; here are our ground rules for their use[J/OL]. Nature, 2023, 613(7945): 612. https://pubmed.ncbi.nlm.nih.gov/36694020/. DOI: 10.1038/d41586-023-00191-1.

[169] Kacena MA, Plotkin LI, Fehrenbacher JC . The Use of Artificial Intelligence in Writing Scientific Review Articles[J/OL]. Current Osteoporosis Reports, 2024, 22(1): 115-121. https://pubmed.ncbi.nlm.nih.gov/38227177/. DOI: 10.1007/s11914-023-00852-0.

[170] Hosseini M, Horbach SPJM . Fighting reviewer fatigue or amplifying bias? Considerations and recommendations for use of ChatGPT and other large language models in scholarly peer review[J/OL]. Research Integrity and Peer Review, 2023, 8(1): 4. https://pubmed.ncbi.nlm.nih.gov/37198671/. DOI: 10.1186/s41073-023-00133-5.

[171] Lee J, Lee J, Yoo JJ . The role of large language models in the peer-review process: opportunities and challenges for medical journal reviewers and editors[J/OL]. Journal of Educational Evaluation for Health Professions, 2025, 22: 4. https://pubmed.ncbi.nlm.nih.gov/40122672/. DOI: 10.3352/jeehp.2025.22.4.

[172] Verharen JP . ChatGPT identifies gender disparities in scientific peer review[J/OL]. eLife, 2023, 12: RP90230. https://pubmed.ncbi.nlm.nih.gov/37922198/. DOI: 10.7554/eLife.90230.

[173] Sebo P. Use of ChatGPT to Explore Gender and Geographic Disparities in Scientific Peer Review[J/OL]. Journal of Medical Internet Research, 2024, 26: e57667. https://pubmed.ncbi.nlm.nih.gov/39652394/. DOI: 10.2196/57667.

[174] Alqahtani T, Badreldin HA, Alrashed M, et al. The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research[J/OL]. Research in Social and Administrative Pharmacy, 2023, 19(8): 1236-1242. https://pubmed.ncbi.nlm.nih.gov/37321925/. DOI: 10.1016/j.sapharm.2023.05.016.

[175] Setzer FC, Li J, Khan AA . The use of artificial intelligence in endodontics[J/OL]. Journal of Dental Research, 2024, 103(9): 853-862. https://pubmed.ncbi.nlm.nih.gov/38822561/. DOI: 10.1177/00220345241255593.

[176] Baaj RE, Alangari TA . Artificial intelligence applications in smile design dentistry: A scoping review[J/OL]. Journal of Prosthodontics, 2025, 34(4): 341-349. https://pubmed.ncbi.nlm.nih.gov/39654301/. DOI: 10.1111/jopr.14000.

[177] Sitaras S, Tsolakis IA, Gelsini M, et al. Applications of Artificial Intelligence in Dental Medicine: A Critical Review[J/OL]. International Dental Journal, 2025, 75(2): 474-486. https://pubmed.ncbi.nlm.nih.gov/39843259/. DOI: 10.1016/j.identj.2024.11.009.

[178] Schneider L, Rischke R, Krois J, et al. Federated vs Local vs Central Deep Learning of Tooth Segmentation on Panoramic Radiographs[J/OL]. Journal of Dentistry, 2023, 135: 104556. https://pubmed.ncbi.nlm.nih.gov/37209769/. DOI: 10.1016/j.jdent.2023.104556.

[179] Raab R, Küderle A, Zakreuskaya A, et al. Federated electronic health records for the European Health Data Space[J/OL]. The Lancet Digital Health, 2023, 5(11): e840-e847. https://pubmed.ncbi.nlm.nih.gov/37741765/. DOI: 10.1016/S2589-7500(23)00156-5.

[180] Truhn D, Tayebi Arasteh S, Saldanha OL, et al. Encrypted federated learning for secure decentralized collaboration in cancer image analysis[J/OL]. Medical Image Analysis, 2024, 92: 103059. https://pubmed.ncbi.nlm.nih.gov/38104402/. DOI: 10.1016/j.media.2023.103059.

[181] Teo ZL, Zhang X, Yang Y, et al. Privacy-Preserving Technology Using Federated Learning and Blockchain in Protecting against Adversarial Attacks for Retinal Imaging[J/OL]. Ophthalmology, 2025, 132(4): 484-494. https://pubmed.ncbi.nlm.nih.gov/39424148/. DOI: 10.1016/j.ophtha.2024.10.017.

[182] Magrabi F, Ammenwerth E, McNair JB, et al. Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications[J/OL]. Yearbook of Medical Informatics, 2019, 28(01): 128-134. https://pubmed.ncbi.nlm.nih.gov/31022752/. DOI: 10.1055/s-0039-1677903.

[183] Chen S, Yang Y, Wu W, et al. Classification of Caries Based on CBCT: A Deep Learning Network Interpretability Study[J/OL]. Journal of Imaging Informatics in Medicine, 2024, 37(6): 3160-3173. https://pubmed.ncbi.nlm.nih.gov/38806951/. DOI: 10.1007/s10278-024-01143-5.

[184] van der Velden BH, Kuijf HJ, Gilhuijs KG, et al. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis[J/OL]. Medical Image Analysis, 2022, 79: 102470. https://pubmed.ncbi.nlm.nih.gov/35576821/. DOI: 10.1016/j.media.2022.102470.

[185] Hamida SU, Chowdhury MJM, Chakraborty NR, et al. Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications[J/OL]. Big Data and Cognitive Computing, 2024, 8(11): 149. https://doi.org/10.3390/bdcc8110149. DOI: 10.3390/bdcc8110149.

[186] Kim D, Kim J, Choi SG . CNN-based remote dental diagnosis model for caries detection with grad-CAM[J/OL]. Scientific Reports, 2025, 15(1): 26555. https://pubmed.ncbi.nlm.nih.gov/40695984/. DOI: 10.1038/s41598-025-11447-3.

[187] Nohara Y, Matsumoto K, Soejima H, et al. Explanation of machine learning models using shapley additive explanation and application for real data in hospital[J/OL]. Computer Methods and Programs in Biomedicine, 2022, 214: 106584. https://pubmed.ncbi.nlm.nih.gov/34942412/. DOI: 10.1016/j.cmpb.2021.106584.

[188] Patel J, Badi M, Katiyar R, et al. SDoH Impact on Periodontal Disease Using Machine Learning and Dental Records[J/OL]. Journal of Dental Research, 2025, 104(10): 1085-1094. https://pubmed.ncbi.nlm.nih.gov/40320624/. DOI: 10.1177/00220345251328968.

[189] Tariq A, Nakhi FB, Salah F, et al. Efficiency and accuracy of artificial intelligence in the radiographic detection of periodontal bone loss: A systematic review[J/OL]. Imaging Science in Dentistry, 2023, 53(3): 193-198. https://pubmed.ncbi.nlm.nih.gov/37799746/. DOI: 10.5624/isd.20230092.

[190] Abbott LP, Saikia A, Anthonappa RP . ARTIFICIAL INTELLIGENCE PLATFORMS IN DENTAL CARIES DETECTION: A SYSTEMATIC REVIEW AND META-ANALYSIS[J/OL]. Journal of Evidence-Based Dental Practice, 2025, 25(1): 102077. https://pubmed.ncbi.nlm.nih.gov/39947783/. DOI: 10.1016/j.jebdp.2024.102077.

[191] Rokhshad R, Karteva T, Chaurasia A, et al. Artificial intelligence and smile design: An e-Delphi consensus statement of ethical challenges[J/OL]. Journal of Prosthodontics, 2024, 33(8): 730-735. https://pubmed.ncbi.nlm.nih.gov/38655727/. DOI: 10.1111/jopr.13858.

[192] Balalle H, Pannilage S . Reassessing academic integrity in the age of AI: A systematic literature review on AI and academic integrity[J/OL]. Social Sciences & Humanities Open, 2025, 11: 101299. https://doi.org/10.1016/j.ssaho.2025.101299. DOI: 10.1016/j.ssaho.2025.101299.

[193] Májovský M, Černý M, Kasal M, et al. Artificial Intelligence Can Generate Fraudulent but Authentic-Looking Scientific Medical Articles: Pandora's Box Has Been Opened[J/OL]. Journal of Medical Internet Research, 2023, 25: e46924. https://pubmed.ncbi.nlm.nih.gov/37256685/. DOI: 10.2196/46924.

[194] Küchemann S, Avila KE, Dinc Y, et al. On opportunities and challenges of large multimodal foundation models in education[J/OL]. npj Science of Learning, 2025, 10(1): 11. https://pubmed.ncbi.nlm.nih.gov/40000649/. DOI: 10.1038/s41539-025-00301-w.

[195] Klimova B, Pikhart M . Exploring the effects of artificial intelligence on student and academic well-being in higher education: a mini-review[J/OL]. Frontiers in Psychology, 2025, 16: 1498132. https://pubmed.ncbi.nlm.nih.gov/39963679/. DOI: 10.3389/fpsyg.2025.1498132.

[196] Ali O, Murray PA, Momin M, et al. The effects of artificial intelligence applications in educational settings: Challenges and strategies[J/OL]. Technological Forecasting and Social Change, 2024, 199: 123076. https://doi.org/10.1016/j.techfore.2023.123076. DOI: 10.1016/j.techfore.2023.123076.

[197] Švab I, Klemenc-Ketiš Z, Zupanič S . New Challenges in Scientific Publications: Referencing, Artificial Intelligence and ChatGPT[J/OL]. Zdravstveno varstvo, 2023, 62(3): 109-112. https://pubmed.ncbi.nlm.nih.gov/37327133/. DOI: 10.2478/sjph-2023-0015.

[198] Rubin M, Arnon H, Huppert JD, et al. Considering the Role of Human Empathy in AI-Driven Therapy[J/OL]. JMIR Mental Health, 2024, 11: e56529. https://pubmed.ncbi.nlm.nih.gov/38861302/. DOI: 10.2196/56529.

[199] Monteith S, Glenn T, Geddes J, et al. Commercial Use of Emotion Artificial Intelligence (AI): Implications for Psychiatry[J/OL]. Current Psychiatry Reports, 2022, 24(3): 203-211. https://pubmed.ncbi.nlm.nih.gov/35212918/. DOI: 10.1007/s11920-022-01330-7.

[200] Elyoseph Z, Refoua E, Asraf K, et al. Capacity of Generative AI to Interpret Human Emotions From Visual and Textual Data: Pilot Evaluation Study[J/OL]. JMIR Mental Health, 2024, 11: e54369. https://pubmed.ncbi.nlm.nih.gov/38319707/. DOI: 10.2196/54369.

[201] Shen J, DiPaola D, Ali S, et al. Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study[J/OL]. JMIR Mental Health, 2024, 11: e62679. https://pubmed.ncbi.nlm.nih.gov/39321450/. DOI: 10.2196/62679.

[202] Rubin M, Li JZ, Zimmerman F, et al. Comparing the value of perceived human versus AI-generated empathy[J/OL]. Nature Human Behaviour, 2025, 9(11): 2345-2359. https://pubmed.ncbi.nlm.nih.gov/40588597/. DOI: 10.1038/s41562-025-02247-w.

[203] Stanley N, Marshall K, Gardiner A . Who is the "Therapist" in Digital Cognitive Behavioural Therapy for Insomnia (dCBTi)?[J/OL]. Nature and Science of Sleep, 2025, 17: 1319-1324. https://pubmed.ncbi.nlm.nih.gov/40535057/. DOI: 10.2147/NSS.S516276.

[204] Shan T, Tay F, Gu L . Application of Artificial Intelligence in Dentistry[J/OL]. Journal of Dental Research, 2021, 100(3): 232-244. https://doi.org/10.1177/0022034520969115. DOI: 10.1177/0022034520969115.