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中华口腔医学研究杂志(电子版) ›› 2026, Vol. 20 ›› Issue (01) : 40 -46. doi: 10.3877/cma.j.issn.1674-1366.2026.01.006

数智口腔专栏·综述

人工智能图像处理技术在口腔医学中的应用
吕思怡1, 王琰琪1, 仇珺2, 陈宇江3, 高洁1,()   
  1. 1口颌系统重建与再生全国重点实验室,国家口腔疾病临床医学研究中心,陕西省口腔疾病临床医学研究中心,空军军医大学口腔医院正畸科,西安 710032
    2口颌系统重建与再生全国重点实验室,国家口腔疾病临床医学研究中心,陕西省口腔疾病临床医学研究中心,空军军医大学口腔医院牙体牙髓病科,西安 710032
    3口颌系统重建与再生全国重点实验室,国家口腔疾病临床医学研究中心,陕西省口腔疾病临床医学研究中心,空军军医大学口腔医院儿童口腔科,西安 710032
  • 收稿日期:2025-12-16 出版日期:2026-02-01
  • 通信作者: 高洁

Application of artificial intelligence image processing technology in stomatology

Siyi Lü1, Yanqi Wang1, Jun Qiu2, Yujiang Chen3, Jie Gao1,()   

  1. 1State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Clinical Research Center for Oral Diseases, Department of Orthodontics, School of Stomatology, Air Force Medical University, Xi'an 710032, China
    2State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Clinical Research Center for Oral Diseases, Department of Cariology and Endodontics, School of Stomatology, Air Force Medical University, Xi'an 710032, China
    3State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Clinical Research Center for Oral Diseases, Department of Pediatric Dentistry, School of Stomatology, Air Force Medical University, Xi'an 710032, China
  • Received:2025-12-16 Published:2026-02-01
  • Corresponding author: Jie Gao
  • Supported by:
    New Clinical Technologies and Services of the Third Affiliated Hospital of Air Force Medical University(LX2022-401); National Clinical Research Center for Oral Diseases Project(LCB202202); Key Project of Shaanxi Province Key Research and Development Program(2024SF-GJHX-37)
引用本文:

吕思怡, 王琰琪, 仇珺, 陈宇江, 高洁. 人工智能图像处理技术在口腔医学中的应用[J/OL]. 中华口腔医学研究杂志(电子版), 2026, 20(01): 40-46.

Siyi Lü, Yanqi Wang, Jun Qiu, Yujiang Chen, Jie Gao. Application of artificial intelligence image processing technology in stomatology[J/OL]. Chinese Journal of Stomatological Research(Electronic Edition), 2026, 20(01): 40-46.

近年来,人工智能(AI)图像处理技术已逐渐成熟并应用于口腔医学领域,有力地推动了口腔医学的发展。本文系统综述了AI图像处理技术在口腔医学中的研究与应用进展,涵盖基础理论、关键技术及临床实践等多个方面。在阐释深度学习、机器学习等核心技术的基础上,重点分析了该技术在各类口腔医学图像中的具体应用,并进一步展望了其未来发展方向、潜在应用价值,以及伴随而来的伦理与法律挑战,为实现更广泛、更可靠的临床应用,仍有诸多关键问题亟待解决。

In recent years, artificial intelligence (AI) image processing technology has matured and been widely applied in stomatology, significantly advancing the field. This article systematically reviews its research and application progress, covering fundamental theories, key technologies, and clinical practices. Based on explanations of technologies including deep learning and machine learning, it focuses on analyzing specific applications across various dental images and discusses future directions, potential value, and associated ethical and legal challenges. To achieve more extensive and reliable clinical applications, key issues remain to be addressed.

图1 人工智能图像处理技术在口腔医学中的应用架构 CBCT:锥形束CT;MR:磁共振。
[1]
王琰琳,李刚.人工智能在口腔疾病影像诊断中的研究进展[J].口腔疾病防治202230(11):816-820. DOI:10.12016/j.issn.2096-1456.2022.11.009.
[2]
Castiglioni IRundo LCodari M,et al. AI applications to medical images:From machine learning to deep learning[J]. Physica Medica202183:9-24. DOI:10.1016/j.ejmp.2021.02.006.
[3]
Chen JYang NPan Y,et al. Synchronous Medical Image Augmentation framework for deep learning-based image segmentation[J]. Comput Med Imaging Graph2023104:102161. DOI:10.1016/j.compmedimag.2022.102161.
[4]
Alkhalid FFHasan AMAlhamady AA. Improving radiographic image contrast using multi layers of histogram equalization technique[J]. IAES International Journal of Artificial Intelligence(IJ-AI)202110(1):151-156. DOI:10.11591/ijai.v10.i1.pp151-156.
[5]
Naumovich SSNaumovich SAGoncharenko VG. Three-dimensional reconstruction of teeth and jaws based on segmentation of CT images using watershed transformation[J]. Dentomaxillofac Radiol201544(4):20140313. DOI:10.1259/dmfr.20140313.
[6]
Verma AAMurray JGreiner R,et al. Implementing machine learning in medicine[J]. CMAJ2021193(34):E1351-E1357. DOI:10.1503/cmaj.202434.
[7]
Chang JChang MFAngelov N,et al. Application of deep machine learning for the radiographic diagnosis of periodontitis[J]. Clin Oral Investig202226(11):6629-6637. DOI:10.1007/s00784-022-04617-4.
[8]
Shen DWu GSuk HI. Deep learning in medical image analysis[J]. Annu Rev Biomed Eng201719:221-248. DOI:10.1146/annurev-bioeng-071516-044442.
[9]
梁蒙蒙,周涛,张飞飞,等.卷积神经网络及其在医学图像分析中的应用研究[J].生物医学工程学杂志201835(6):977-985. DOI:10.7507/1001-5515.201710060.
[10]
Nassiri KAkhloufi MA. YOLO-based panoramic dental X-ray image analysis[J]. Neural Comput Appl202537(31):25867-25890. DOI:10.1007/s00521-025-11462-5.
[11]
Almalki SAAlsubai SAlqahtani A,et al. Denoised encoder-based residual U-net for precise teeth image segmentation and damage prediction on panoramic radiographs[J]. J Dent2023137:104651. DOI:10.1016/j.jdent.2023.104651.
[12]
Hegazy MAACho MHLee SY. Half-scan artifact correction using generative adversarial network for dental CT[J]. Comput Biol Med2021132:104313. DOI:10.1016/j.compbiomed.2021.104313.
[13]
Lee JHKim DHJeong SN,et al. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm[J]. J Dent201877:106-111. DOI:10.1016/j.jdent.2018.07.015.
[14]
Oztekin FKatar OSadak F,et al. An explainable deep learning model to prediction dental caries using panoramic radiograph images[J]. Diagnostics202313(2):226. DOI:10.3390/diagnostics13020226.
[15]
Issa JJaber MRifai I,et al. Diagnostic test accuracy of artificial intelligence in detecting periapical periodontitis on two-dimensional radiographs:A retrospective study and literature review[J]. Medicina(Kaunas)202359(4):768. DOI:10.3390/medicina59040768.
[16]
Saber SNasr HAETaha N,et al. Classification of apical periodontitis with YOLOv5 and YOLOv8 architectures[J]. Int Dent J202474:S99. DOI:10.1016/j.identj.2024.07.873.
[17]
Fukuda MInamoto KShibata N,et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography[J]. Oral Radiol202036(4):337-343. DOI:10.1007/s11282-019-00409-x.
[18]
Ramezanzade SDascalu TLIbragimov B,et al. Prediction of pulp exposure before caries excavation using artificial intelligence:Deep learning-based image data versus standard dental radiographs[J]. J Dent2023138:104732. DOI:10.1016/j.jdent.2023.104732.
[19]
Hiraiwa TAriji YFukuda M,et al. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography[J]. Dentomaxillofac Radiol201948(3):20180218. DOI:10.1259/dmfr.20180218.
[20]
Kurt-Bayrakdar SBayrakdar İŞYavuz MB,et al. Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm:A retrospective study[J]. BMC Oral Health202424(1):155. DOI:10.1186/s12903-024-03896-5.
[21]
Jundaeng JChamchong RNithikathkul C. Artificial intelligence-powered innovations in periodontal diagnosis:A new era in dental healthcare[J]. Front Med Technol20256:1469852. DOI:10.3389/fmedt.2024.1469852.
[22]
Xue TChen LSun Q. Deep learning method to automatically diagnose periodontal bone loss and periodontitis stage in dental panoramic radiograph[J]. J Dent2024150:105373. DOI:10.1016/j.jdent.2024.105373.
[23]
Chiang HMJonzén KWu WYY,et al. How accurate is AI in detecting marginal jaw bone loss? A systematic review and Meta-analysis[J]. J Dent2025163:106151. DOI:10.1016/j.jdent.2025.106151.
[24]
Lee JPark JLee S,et al. Automated diagnosis for extraction difficulty of maxillary and mandibular third molars and post-extraction complications using deep learning[J]. Sci Rep202515(1):19036. DOI:10.1038/s41598-025-00236-7.
[25]
Celik ME. Deep learning based detection tool for impacted mandibular third molar teeth[J]. Diagnostics(Basel)202212(4):942. DOI:10.3390/diagnostics12040942.
[26]
Kayadibi İKöse UGüraksin GE,et al. An AI-assisted explainable mTMCNN architecture for detection of mandibular third molar presence from panoramic radiography[J]. Int J Med Inform2025195:105724. DOI:10.1016/j.ijmedinf.2024.105724.
[27]
Zhu TChen DWu F,et al. Artificial intelligence model to detect real contact relationship between mandibular third molars and inferior alveolar nerve based on panoramic radiographs[J]. Diagnostics(Basel)202111(9):1664. DOI:10.3390/diagnostics11091664.
[28]
Rašić MTropčić MKarlović P,et al. Detection and segmentation of radiolucent lesions in the lower jaw on panoramic radiographs using deep neural networks[J]. Medicina(Kaunas)202359(12):2138. DOI:10.3390/medicina59122138.
[29]
Alsmadi MK. A hybrid Fuzzy C-Means and Neutrosophic for jaw lesions segmentation[J]. Ain Shams Engineering Journal20189(4):697-706. DOI:10.1016/j.asej.2016.03.016.
[30]
Çoban DYaşa YAktaş A,et al. Detection of jaw lesions on panoramic radiographs using deep learning method[J]. J Imaging Inform Med2025. DOI:10.1007/s10278-025-01642-z.
[31]
Watanabe HAriji YFukuda M,et al. Deep learning object detection of maxillary cyst-like lesions on panoramic radiographs:Preliminary study[J]. Oral Radiol202137(3):487-493. DOI:10.1007/s11282-020-00485-4.
[32]
ver Berne JSaadi SBPolitis C,et al. A deep learning approach for radiological detection and classification of radicular cysts and periapical granulomas[J]. J Dent2023135:104581. DOI:10.1016/j.jdent.2023.104581.
[33]
Poedjiastoeti WSuebnukarn S. Application of convolutional neural network in the diagnosis of jaw tumors[J]. Healthc Inform Res201824(3):236-241. DOI:10.4258/hir.2018.24.3.236.
[34]
Lee AKim MSHan SS,et al. Deep learning neural networks to differentiate Stafne's bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography[J]. PLoS One202116(7):e0254997. DOI:10.1371/journal.pone.0254997.
[35]
Leonardi RGiordano DMaiorana F. An evaluation of cellular neural networks for the automatic identification of cephalometric landmarks on digital images[J]. J Biomed Biotechnol2009:717102. DOI:10.1155/2009/717102.
[36]
Yao JZeng WHe T,et al. Automatic localization of cephalometric landmarks based on convolutional neural network[J]. Am J Orthod Dentofacial Orthop2022161(3):e250-e259. DOI:10.1016/j.ajodo.2021.09.012.
[37]
Lee JHYu HJKim MJ,et al. Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks[J]. BMC Oral Health202020(1):270. DOI:10.1186/s12903-020-01256-7.
[38]
Hwang HWPark JHMoon JH,et al. Automated identification of cephalometric landmarks:Part 2—Might it be better than human?[J]. Angle Orthod202090(1):69-76. DOI:10.2319/022019-129.1.
[39]
Song YBJeong HGKim C,et al. Comparison of detection performance of soft tissue calcifications using artificial intelligence in panoramic radiography[J]. Sci Rep202212(1):19115. DOI:10.1038/s41598-022-22595-1.
[40]
Wu PYLin YJChang YJ,et al. Deep learning-assisted diagnostic system:Apices and odontogenic sinus floor level analysis in dental panoramic radiographs[J]. Bioengineering (Basel)202512(2):134. DOI:10.3390/bioengineering12020134.
[41]
Choi EShin SLee K,et al. Artificial intelligence-enhanced diagnosis of degenerative joint disease using temporomandibular joint panoramic radiography and joint noise data[J]. Sci Rep202515(1):1823. DOI:10.1038/s41598-024-83750-4.
[42]
Yuksel IBBoudesh AGhanbarzadehchaleshtori M,et al. Artificial intelligence-assisted identification of condensing osteitis and idiopathic osteosclerosis on panoramic radiographs[J]. Sci Rep202515(1):29407. DOI:10.1038/s41598-025-15451-5.
[43]
Zheng ZYan HSetzer FC,et al. Anatomically constrained deep learning for automating dental CBCT segmentation and lesion detection[J]. IEEE Transactions on Automation Science and Engineering202118(2):603-614. DOI:10.1109/TASE.2020.3025871.
[44]
Wajer RWajer AKazimierczak N,et al. The impact of AI on metal artifacts in CBCT oral cavity imaging[J]. Diagnostics (Basel)202414(12):1280. DOI:10.3390/diagnostics14121280.
[45]
Lahoud PDiels SNiclaes L,et al. Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT[J]. J Dent2022116:103891. DOI:10.1016/j.jdent.2021.103891.
[46]
Özemre Bektaş JYanik H,et al. Enhanced diagnostic pipeline for maxillary sinus-maxillary molars relationships:A novel implementation of Detectron2 with faster R-CNN R50 FPN 3x on CBCT images[J]. BMC Oral Health202525(1):1473. DOI:10.1186/s12903-025-06337-z.
[47]
Lin XFu YRen G,et al. Micro-computed tomography-guided artificial intelligence for pulp cavity and tooth segmentation on cone-beam computed tomography[J]. J Endod202147(12):1933-1941. DOI:10.1016/j.joen.2021.09.001.
[48]
Zanini LGKRubira-Bullen IRFNunes FLDS. Enhancing dental caries classification in CBCT images by using image processing and self-supervised learning[J]. Comput Biol Med2024183:109221. DOI:10.1016/j.compbiomed.2024.109221.
[49]
Ezhov MGusarev MGolitsyna M,et al. Clinically applicable artificial intelligence system for dental diagnosis with CBCT[J]. Sci Rep202111(1):15006. DOI:10.1038/s41598-021-94093-9.
[50]
Kirnbauer BHadzic AJakse N,et al. Automatic detection of periapical osteolytic lesions on cone-beam computed tomography using deep convolutional neuronal networks[J]. J Endod202248(11):1434-1440. DOI:10.1016/j.joen.2022.07.013.
[51]
Hu ZCao DHu Y,et al. Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images[J]. BMC Oral Health202222(1):382. DOI:10.1186/s12903-022-02422-9.
[52]
Reduwan NHAziz AAMohd Razi R,et al. Application of deep learning and feature selection technique on external root resorption identification on CBCT images[J]. BMC Oral Health202424(1):252. DOI:10.1186/s12903-024-03910-w.
[53]
Talaat WMShetty SAl Bayatti S,et al. An artificial intelligence model for the radiographic diagnosis of osteoarthritis of the temporomandibular joint[J]. Sci Rep202313(1):15972. DOI:10.1038/s41598-023-43277-6.
[54]
Chai ZKMao LChen H,et al. Improved diagnostic accuracy of ameloblastoma and odontogenic keratocyst on cone-beam CT by artificial intelligence[J]. Front Oncol202211:793417. DOI:10.3389/fonc.2021.793417.
[55]
Jiao XGao SHuang F,et al. An improved algorithm for full-mouth lesion detection based on YOLOv8[J]. Graphical Models2025142:101302. DOI:10.1016/j.gmod.2025.101302.
[56]
Xiao YLiang QZhou L,et al. Construction of a new automatic grading system for jaw bone mineral density level based on deep learning using cone beam computed tomography[J]. Sci Rep202212(1):12841. DOI:10.1038/s41598-022-16074-w.
[57]
Dilip Taide PFaizan MSalunkhe G,et al. Automated prediction of dental implant success using a mask region-based convolutional neural network on preoperative cone-beam computed tomography scans[J]. Cureus202517(9):e93378. DOI:10.7759/cureus.93378.
[58]
Hu XZhao YYang C. Evaluation of root position during orthodontic treatment via multiple intraoral scans with automated registration technology[J]. Am J Orthod Dentofacial Orthop2023164(2):285-292. DOI:10.1016/j.ajodo.2023.04.012.
[59]
Zhu MYang PBian C,et al. Convolutional neural network-assisted diagnosis of midpalatal suture maturation stage in cone-beam computed tomography[J]. J Dent2024141:104808. DOI:10.1016/j.jdent.2023.104808.
[60]
Zheng YMLi JLiu S,et al. MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland[J]. Eur Radiol202131(6):4042-4052. DOI:10.1007/s00330-020-07483-4.
[61]
Xia XFeng BWang J,et al. Deep learning for differentiating benign from malignant parotid lesions on mr images[J]. Front Oncol202111:632104. DOI:10.3389/fonc.2021.632104.
[62]
Chang YJHuang TYLiu YJ,et al. Classification of parotid gland tumors by using multimodal MRI and deep learning[J]. NMR Biomed202034(1):e4408. DOI:10.1002/nbm.4408.
[63]
Kao ZKChiu NTWu HTH,et al. Classifying temporomandibular disorder with artificial intelligent architecture using magnetic resonance imaging[J]. Ann Biomed Eng202351(3):517-526. DOI:10.1007/s10439-022-03056-2.
[64]
Lee YHWon JHKim S,et al. Advantages of deep learning with convolutional neural network in detecting disc displacement of the temporomandibular joint in magnetic resonance imaging[J]. Sci Rep202212(1):11352. DOI:10.1038/s41598-022-15231-5.
[65]
Yang LZhang SLi J,et al. Diagnosis of lymph node metastasis in oral squamous cell carcinoma by an MRI-based deep learning model[J]. Oral Oncol2025161:107165. DOI:10.1016/j.oraloncology.2024.107165.
[66]
Vukicevic AMMilic VZabotti A,et al. Radiomics-based assessment of primary Sjögren's syndrome from salivary gland ultrasonography images[J]. IEEE J Biomed Health Inform202024(3):835-843. DOI:10.1109/JBHI.2019.2923773.
[67]
Badea AFBran STamas-Szora A,et al. Solid parotid tumors:An individual and integrative analysis of various ultrasonographic criteria. A prospective and observational study[J]. Med Ultrason201315(4):289-298. DOI:10.11152/mu.2013.2066.154.afb2.
[68]
Keser GYülek HÖner Talmaç AG,et al. A deep learning approach for mandibular condyle segmentation on ultrasonography[J]. J Imaging Inform Med2025. DOI:10.1007/s10278-025-01527-1.
[69]
Fati SMSenan EMJaved Y. Early diagnosis of oral squamous cell carcinoma based on histopathological images using deep and hybrid learning approaches[J]. Diagnostics(Basel)202212(8):1899. DOI:10.3390/diagnostics12081899.
[70]
Yang SYLi SHLiu JL,et al. Histopathology-based diagnosis of oral squamous cell carcinoma using deep learning[J]. J Dent Res2022101(11):1321-1327. DOI:10.1177/00220345221089858.
[71]
El-Aziz AAAMahmood MAEl-Ghany SA. Enhancing early detection of oral squamous cell carcinoma:A deep learning approach with LRT-enhanced EfficientNet-B3 for accurate and efficient histopathological diagnosis[J]. Diagnostics202515(13):1678. DOI:10.3390/diagnostics15131678.
[72]
Sukegawa SOno STanaka F,et al. Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists[J]. Sci Rep202313(1):11676. DOI:10.1038/s41598-023-38343-y.
[73]
Shephard AJMahmood HRaza SEA,et al. Development and validation of an artificial intelligence-based pipeline for predicting oral epithelial dysplasia malignant transformation[J]. Commun Med(Lond)20255(1):186. DOI:10.1038/s43856-025-00873-z.
[74]
Esce ARBaca ALRedemann JP,et al. Predicting nodal metastases in squamous cell carcinoma of the oral tongue using artificial intelligence[J]. Am J Otolaryngol202445(1):104102. DOI:10.1016/j.amjoto.2023.104102.
[75]
Talwar VSingh PMukhia N,et al. AI-Assisted screening of oral potentially malignant disorders using smartphone-based photographic images[J]. Cancers(Basel)202315(16):4120. DOI:10.3390/cancers15164120.
[76]
Rabinovici-Cohen SFridman NWeinbaum M,et al. From pixels to diagnosis:Algorithmic analysis of clinical oral photos for early detection of oral squamous cell carcinoma[J]. Cancers (Basel)202416(5):1019. DOI:10.3390/cancers16051019.
[77]
Park EYCho HKang S,et al. Caries detection with tooth surface segmentation on intraoral photographic images using deep learning[J]. BMC Oral Health202222(1):573. DOI:10.1186/s12903-022-02589-1.
[78]
Yoon KJeong HMKim JW,et al. AI-based dental caries and tooth number detection in intraoral photos:Model development and performance evaluation[J]. J Dent2024141:104821. DOI:10.1016/j.jdent.2023.104821.
[79]
Liu YCheng YSong Y,et al. Oral screening of dental calculus,gingivitis and dental caries through segmentation on intraoral photographic images using deep learning[J]. BMC Oral Health202424(1):1287. DOI:10.1186/s12903-024-05072-1.
[80]
Chau RCWLi GHTew IM,et al. Accuracy of artificial intelligence-based photographic detection of gingivitis[J]. Int Dent J202373(5):724-730. DOI:10.1016/j.identj.2023.03.007.
[81]
Zhou MJie WTang F,et al. Deep learning algorithms for classification and detection of recurrent aphthous ulcerations using oral clinical photographic images[J]. J Dent Sci202419(1):254-260. DOI:10.1016/j.jds.2023.04.022.
[82]
Keser GBayrakdar İŞPekiner FN,et al. A deep learning algorithm for classification of oral lichen planus lesions from photographic images:A retrospective study[J]. J Stomatol Oral Maxillofac Surg2023124(1):101264. DOI:10.1016/j.jormas.2022.08.007.
[83]
Farag AElhabian SAbdelrehim A,et al. Model-based human teeth shape recovery from a single optical image with unknown illumination[C/OL]//Menze BHLangs GLu L,et al. Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. Berlin,Heidelberg:Springer,2013:263-272. DOI:10.1007/978-3-642-36620-8_26.
[84]
Abdelrahim ASEl-Melegy MTFarag AA. Realistic 3D reconstruction of the human teeth using shape from shading with shape priors[C/OL]//2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence,RI,USA,2012:64-69. DOI:10.1109/CVPRW.2012.6239249.
[85]
Chen YGao STu P,et al. Automatic 3D teeth reconstruction from five intra-oral photos using parametric teeth model[J]. IEEE Trans Vis Comput Graph202430(8):4780-4791. DOI:10.1109/TVCG.2023.3277914.
[86]
Perdoncini NNSchussel JLAmenábar JM,et al. Use of smartphone video calls in the diagnosis of oral lesions:Teleconsultations between a specialist and patients assisted by a general dentist[J]. J Am Dent Assoc2021152(2):127-135. DOI:10.1016/j.adaj.2020.10.013.
[87]
Chen YEsmaeilzadeh P. Generative AI in medical practice:In-depth exploration of privacy and security challenges[J]. J Med Internet Res202426(1):e53008. DOI:10.2196/53008.
[88]
Maliha GGerke SCohen IG,et al. Artificial intelligence and liability in medicine:Balancing safety and innovation[J]. Milbank Q202199(3):629-647. DOI:10.1111/1468-0009.12504.
[89]
刘舒钰,何静晨,唐珺书,等.人工智能在牙体牙髓病学中的应用研究进展[J].中国实用口腔科杂志202518(3):343-350. DOI:10.19538/j.kq.2025.03.014.
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