Objective: The current advancements in digital workflows and artificial intelligence in estimating tissue overload, restoration failures, and longevity predictions have increased the biomechanical precision in the field of esthetic dental rehabilitation and aim to analyze and compare the biomechanical parameters of aesthetic dental rehabilitation using generative neural networks versus traditional clinician-driven methods.

Methodology: A narrative review from 2015 to 2025 examined the PubMed, Scopus, Web of Science, IEEE Xplore, and Cochrane library for studies in English concerning finite element, laboratory, clinical, and AI studies that include the outcomes of biomechanics.

Results: GNN assisted planning showed significant biomechanical gains in intricate rehabilitations, especially in multi-unit and implant-supported restorations. Declined peak stresses and more uniform distribution in peri-implant and periodontal structures, improved control of deformations, and thorough reconstruction in more optimized geometries, especially in the stresses or deformations, were noted. The evidence is mostly simulation-based, methodologically heterogeneous, and lacks thorough and sustained clinical validation.

Conclusions: GNN-based planning indicates possible biomechanical advantages, notably less peak stress for complicated dental rehabilitations; however, most evidence is simulation-based. The GNN workflow's clinical implementation requires biomechanical principles and relies on predictive analysis, explainable AI, and cross-disciplinary substantiation.

aesthetic dental rehabilitation, biomechanical parameters, artificial intelligence, generative neural networks, finite element analysis

Ahmed, R. (2025). The role of artificial intelligence in modern dentistry: Transforming diagnosis and treatment planning. Premier Journal of Dentistry. https://doi.org/10.70389/pjd.100001

Alaida, W. S., Gadi, S. A., Al-Ghannam, R. E., Alamri, M. F., Mirdad, F. I., Argaibeh, R. M., Alqahtani, B. A., Alqahtani, A. M., Al Jaban, A. A., Alkuraydimi, T. M., & Alamari, A. S. (2025). Biomechanical and occlusal factors influencing the longevity of single-unit restorations: A comprehensive review. Cureus, 17(6), e85998. https://doi.org/10.7759/cureus.85998

Al-Matrafi, T., Ahmed, K., Basunbul, G., Ismail, M., & Melaibari, A. (2025). Enhanced implant longevity: Finite element optimization of all-on-four implant designs to minimize jawbone stresses. The Saudi Dental Journal, 37(10–12), 90. https://doi.org/10.1007/s44445-025-00099-8

Alqutaibi, A. Y., Algabri, R., Ibrahim, W. I., Alhajj, M. N., & Elawady, D. (2025). Dental implant planning using artificial intelligence: A systematic review and meta-analysis. The Journal of Prosthetic Dentistry, 134(5), 1619–1629. https://doi.org/10.1016/j.prosdent.2024.03.032

Alresheedi, B. (2022). Digitally guided workflow for the esthetic and prosthetic oral rehabilitation of a patient with excessive gingival display: A case report. Clinical, Cosmetic and Investigational Dentistry, 14, 281–287. https://doi.org/10.2147/CCIDE.S381639

Alwabel, L. K., Alabduljabar, W. M., Alawfi, L. K., Al Johani, N. K., Alharbi, L. E., Kharif, M. A., Albahrani, F. D., Alarjani, M. B., Alzahrani, M. S., Alshammari, A. A., Alashjai, A., & Alanaz, L., Sr. (2025). Digital Smile Design and patient-centered outcomes in esthetic restorative dentistry: A systematic review. Cureus, 17(11), e98084. https://doi.org/10.7759/cureus.98084

Arjumand, B. (2024). The Application of artificial intelligence in restorative Dentistry: A narrative review of current research. The Saudi Dental Journal, 36(6), 835–840. https://doi.org/10.1016/j.sdentj.2024.03.017

Attik, N., Richert, R., & Garoushi, S. (2024). Biomechanics, Bioactive and Biomimetic philosophy in restorative dentistry ̶ Quo vadis? Journal of Dentistry, 148(105036), 105036. https://doi.org/10.1016/j.jdent.2024.105036

Babaei, B., Shouha, P., Birman, V., Farrar, P., Prentice, L., & Prusty, G. (2022). The effect of dental restoration geometry and material properties on biomechanical behaviour of a treated molar tooth: A 3D finite element analysis. Journal of the Mechanical Behavior of Biomedical Materials, 125(104892), 104892. https://doi.org/10.1016/j.jmbbm.2021.104892

Berzaghi, A., Testori, T., Scaini, R., & Bortolini, S. (2025). Occlusion and biomechanical risk factors in implant-supported full-arch fixed dental prostheses-narrative review. Journal of Personalized Medicine, 15(2), 65. https://doi.org/10.3390/jpm15020065

Beyaz, S., Özgözen, A. L., Turgut, N., & Ölke, H. C. (2025). Artificial intelligence and robotic surgery in clinical medicine: progress, challenges, and future directions. Future Science OA, 11(1), 2540742. https://doi.org/10.1080/20565623.2025.2540742

Borba, M., Duan, Y., Griggs, J. A., Cesar, P. F., & Della Bona, Á. (2015). Effect of ceramic infrastructure on the failure behavior and stress distribution of fixed partial dentures. Dental Materials: Official Publication of the Academy of Dental Materials, 31(4), 413–422. https://doi.org/10.1016/j.dental.2015.01.008

Brizuela-Velasco, A., Fernández-Hernández, S., Gaviria-De La Puerta, J. F., & Barbosa, T. (2025). Biomechanics in dental implants: Analysis using artificial intelligence systems. International Journal of Odontostomatology, 19(3), 224–233. https://ijodontostomatology.com/wp-content/uploads/2025/09/2025_v19n3-_006.pdf

Broll, A., Goldhacker, M., Hahnel, S., & Rosentritt, M. (2024). Generative deep learning approaches for the design of dental restorations: A narrative review. Journal of Dentistry, 145(104988), 104988. https://doi.org/10.1016/j.jdent.2024.104988

Broll, A., Rosentritt, M., Schlegl, T., & Goldhacker, M. (2024). A data-driven approach for the partial reconstruction of individual human molar teeth using generative deep learning. Frontiers in Artificial Intelligence, 7, 1339193. https://doi.org/10.3389/frai.2024.1339193

Chang, C.-L., Chen, J.-J., & Chen, C.-S. (2025). Using optimization approach to design dental implant in three types of bone quality - A finite element analysis. Journal of Dental Sciences, 20(1), 126–136. https://doi.org/10.1016/j.jds.2024.09.017

Che, S.-A., Yang, B.-E., Park, S.-Y., On, S.-W., Lim, H.-K., Lee, C.-U., Kim, M.-K., & Byun, S.-H. (2025). Clinical evaluation of AI-based three-dimensional dental implant planning: A multicenter study. Journal of Dentistry, 162(106066), 106066. https://doi.org/10.1016/j.jdent.2025.106066

Chen, H., Li, W., Gao, X., & Xiao, B. (2023). AEP-GAN: Aesthetic Enhanced Perception Generative Adversarial Network for Asian facial beauty synthesis. Applied Intelligence, 53(17), 20441–20468. https://doi.org/10.1007/s10489-023-04576-7

Chen, Y.-C., Lin, J.-W., Wang, K.-H., & Lin, C.-L. (2025). Real-time optimization of prosthetic design for complete arch implant-supported treatments using finite element-based machine learning. The Journal of Prosthetic Dentistry, 134(5), 1920–1928. https://doi.org/10.1016/j.prosdent.2024.06.012

Chisnoiu, A. M., Staicu, A. C., Kui, A., Chisnoiu, R. M., Iacob, S., Fluerașu, M., & Buduru, S. (2023). Smile design and treatment planning-conventional versus digital-A pilot study. Journal of Personalized Medicine, 13(7), 1028. https://doi.org/10.3390/jpm13071028

Desai, S. R., Koulgikar, K. D., Alqhtani, N. R., Alqahtani, A. R., Alqahtani, A. S., Alenazi, A., Heboyan, A., Fernandes, G. V. O., & Mustafa, M. (2023). Three-dimensional FEA analysis of the stress distribution on titanium and graphene frameworks supported by 3 or 6-implant models. Biomimetics (Basel, Switzerland), 8(1), 15. https://doi.org/10.3390/biomimetics8010015

Dhammayannarangsi, P., Na Lampang, S., Tompkins, K. A., Everts, V., Osathanon, T., Limjeerajarus, C. N., & Limjeerajarus, N. (2025). Using the appropriate modulus of elasticity of periodontal ligament matters in stress analysis of human first premolar tooth and periodontium structures. Scientific Reports, 15(1), 1549. https://doi.org/10.1038/s41598-025-85578-y

Dhopte, A., & Bagde, H. (2023). Smart smile: Revolutionizing dentistry with artificial intelligence. Cureus, 15(6), e41227. https://doi.org/10.7759/cureus.41227

Dindorf, C., Dully, J., Konradi, J., Wolf, C., Becker, S., Simon, S., Huthwelker, J., Werthmann, F., Kniepert, J., Drees, P., Betz, U., & Fröhlich, M. (2024). Enhancing biomechanical machine learning with limited data: generating realistic synthetic posture data using generative artificial intelligence. Frontiers in Bioengineering and Biotechnology, 12, 1350135. https://doi.org/10.3389/fbioe.2024.1350135

Ding, H., Cui, Z., Maghami, E., Chen, Y., Matinlinna, J. P., Pow, E. H. N., Fok, A. S. L., Burrow, M. F., Wang, W., & Tsoi, J. K. H. (2023). Morphology and mechanical performance of dental crown designed by 3D-DCGAN. Dental Materials: Official Publication of the Academy of Dental Materials, 39(3), 320–332. https://doi.org/10.1016/j.dental.2023.02.001

Ding, H., Wu, J., Zhao, W., Matinlinna, J. P., Burrow, M. F., & Tsoi, J. K. H. (2023). Artificial intelligence in dentistry-A review. Frontiers in Dental Medicine, 4, 1085251. https://doi.org/10.3389/fdmed.2023.1085251

Dogru, S. C., Cansiz, E., & Arslan, Y. Z. (2018). A review of finite element applications in oral and maxillofacial biomechanics. Journal of Mechanics in Medicine and Biology, 18(02), 1830002. https://doi.org/10.1142/s0219519418300028

Dong, Z., Chen, J., & Xu, Y. (2024). Transformer-based tooth alignment prediction with occlusion and collision constraints. In arXiv [cs.CV]. https://doi.org/10.48550/ARXIV.2410.20806

El-Hakim, M., Khaled, H., Fawzy, A., & Anthonappa, R. (2025). Clinician-led development and feasibility of a neural network for assessing 3D dental cavity preparations assisted by conversational AI. Dentistry Journal, 13(11), 531. https://doi.org/10.3390/dj13110531

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z

Fan, W., Zhang, J., Wang, N., Li, J., & Hu, L. (2023). The application of deep learning on CBCT in dentistry. Diagnostics (Basel, Switzerland), 13(12), 2056. https://doi.org/10.3390/diagnostics13122056

Fatima, A., Shafi, I., Afzal, H., Díez, I. D. L. T., Lourdes, D. R.-S. M., Breñosa, J., Espinosa, J. C. M., & Ashraf, I. (2022). Advancements in dentistry with artificial intelligence: Current clinical applications and future perspectives. Healthcare (Basel, Switzerland), 10(11), 2188. https://doi.org/10.3390/healthcare10112188

Flores-Ramírez, B., Suaste-Gómez, E., García-Limón, V., & Angeles-Medina, F. (2025). Flexible PVDF sensors for bruxism bite force measurement: A redefined instrumental approach. PloS One, 20(8), e0330422. https://doi.org/10.1371/journal.pone.0330422

Franceschini, C., Ahmadi, M., Zhang, X., Wu, K., Lin, M., Weston, R., Rodio, A., Tang, Y., Engeberg, E., Pires, G., Cheema, T. S., & Vrionis, F. D. (2025). Revolutionizing spine surgery with emerging AI-FEA integration. Journal of Robotic Surgery, 19(1), 615. https://doi.org/10.1007/s11701-025-02772-w

Ghafari, J. G., Macari, A. T., Zeno, K. G., & Haddad, R. V. (2020). Potential and limitations of orthodontic biomechanics: recognizing the gaps between knowledge and practice. Journal of the World Federation of Orthodontists, 9(3S), S31–S39. https://doi.org/10.1016/j.ejwf.2020.08.008

Gomes, G. H., Corbellini, A. de O., Rotta, W. G., Martos, J., & Boeira, G. F. (2021). Interdisciplinary esthetic approach in clinical dental rehabilitation. Journal of Conservative Dentistry, 24(5), 519–523. https://doi.org/10.4103/jcd.jcd_441_21

Gunwal, M. K., Shenoi, P. R., Paranjape, T., Dhote, S., Tongya, R., Kumar, M., & Rastogi, S. (2018). Evaluation of fracture resistance and mode of failure of premolars restored with nanohybrid composite, ORMOCER and ceramic inlays. Journal of Oral Biology and Craniofacial Research, 8(2), 134–139. https://doi.org/10.1016/j.jobcr.2017.08.004

Gupta, M., Madhok, K., Kulshrestha, R., Chain, S., Kaur, H., & Yadav, A. (2020). Determination of stress distribution on periodontal ligament and alveolar bone by various tooth movements - A 3D FEM study. Journal of Oral Biology and Craniofacial Research, 10(4), 758–763. https://doi.org/10.1016/j.jobcr.2020.10.011

He, J. T. (2025). A review of fatigue performance and failure mechanisms in dental restorative materials under dynamic occlusal loading. Journal of Neonatal Surgery, 14(25S), 958–993. https://www.jneonatalsurg.com/index.php/jns/article/view/6233

Holst, S., Geiselhoeringer, H., Wichmann, M., & Holst, A. I. (2008). The effect of provisional restoration type on micromovement of implants. The Journal of Prosthetic Dentistry, 100(3), 173–182. https://doi.org/10.1016/S0022-3913(08)60176-6

Hussain, S. A., Bresnahan, M., & Zhuang, J. (2025). Can artificial intelligence revolutionize healthcare in the Global South? A scoping review of opportunities and challenges. Digital Health, 11, 20552076251348024. https://doi.org/10.1177/20552076251348024

Joda, T., & Zitzmann, N. U. (2022). Personalized workflows in reconstructive dentistry-current possibilities and future opportunities. Clinical Oral Investigations, 26(6), 4283–4290. https://doi.org/10.1007/s00784-022-04475-0

Joda, T., Balmer, M., Jung, R. E., & Ioannidis, A. (2024). Clinical use of digital applications for diagnostic and treatment planning in prosthodontics: A scoping review. Clinical Oral Implants Research, 35(8), 782–792. https://doi.org/10.1111/clr.14230

Jubhari, E. H., & Aenun, N. (2020). Complete rehabilitation using a digital smile design combined with conventional restorative materials and CAD/CAM in bruxism patient. Makassar Dental Journal, 9(3), 233–239. https://doi.org/10.35856/mdj.v9i3.363

Kabbin, R. B., Moldi, A. I., Bulla, C. S., Eklarkar, P. K., Patil, S. S., & Biradar, P. S. (2025). A comparative analysis of complete coverage tooth preparation design and scan angulation on the accuracy of two different intra-oral digital scanners: A 3-dimensional study. Journal of Pharmacy & Bioallied Sciences. https://doi.org/10.4103/jpbs.jpbs_1188_25

Kaviandost, P., Barkhordar, S., & Asghari, M. (2025). Revolutionizing dentistry: The role of artificial intelligence in diagnosis, treatment planning, and patient care. Saudi Journal of Oral and Dental Research, 10(06), 260–266. https://doi.org/10.36348/sjodr.2025.v10i06.002

Khan, M. A., Ansari, A. S., & Anwar, M. (2024). Role of artificial intelligence in dental practice -A narrative review. Pak. J. Med. Dent. https://doi.org/10.36283/ziun-pjmd13-4/023

Khan, M. K. (2025). Three-dimensional finite element analysis of biomechanical effects of orthodontic clear aligners in maxillary arch expansion procedure: A comprehensive review of the current literature. Journal of Datta Meghe Institute of Medical Sciences University, 20(3), 503–512. https://doi.org/10.4103/jdmimsu.jdmimsu_219_25

Khanna, N. (2020). Functional aesthetic dentistry: How to achieve predictable aesthetic results using principles of a stable occlusion (2020th ed.). Springer Nature. https://doi.org/10.1007/978-3-030-39115-7

Kinney, J. H., Marshall, S. J., & Marshall, G. W. (2003). The mechanical properties of human dentin: a critical review and re-evaluation of the dental literature. Critical reviews in oral biology and medicine : an official publication of the American Association of Oral Biologists, 14(1), 13–29. https://doi.org/10.1177/154411130301400103

Knoernschild, K. L. (2020). Parameters of care for the specialty of prosthodontics. Journal of Prosthodontics, 29(S1), 3–147. https://doi.org/10.1111/jopr.13176

Koppireddy, C. S., & Rao, S. N. (2025). Prediction of geometric features in 3D facial soft tissue images using fully connected neural network. Journal of Theoretical and Applied Information Technology, 103(9), 3587–3595. https://www.jatit.org/volumes/Vol103No9/4Vol103No9.pdf

Kriswanto, K., Jamari, J., Andika, R., Bayuseno, A. P., Yusuf, A. A., & Ammarullah, M. I. (2025). Fixture design effects on posterior dental implant stability using finite element analysis (FEA): a systematic review. Head & Face Medicine, 22(1), 2. https://doi.org/10.1186/s13005-025-00566-x

Kumar, A., Aelgani, V., Vohra, R., Gupta, S. K., Bhagawati, M., Paul, S., Saba, L., Suri, N., Khanna, N. N., Laird, J. R., Johri, A. M., Kalra, M., Fouda, M. M., Fatemi, M., Naidu, S., & Suri, J. S. (2023). Artificial intelligence bias in medical system designs: a systematic review. Multimedia Tools and Applications, 83(6), 18005–18057. https://doi.org/10.1007/s11042-023-16029-x

Lee, S. J., Poon, J., Jindarojanakul, A., Huang, C.-C., Viera, O., Cheong, C. W., & Lee, J. D. (2025). Artificial intelligence in dentistry: Exploring emerging applications and future prospects. Journal of Dentistry, 155(105648), 105648. https://doi.org/10.1016/j.jdent.2025.105648

Li, Z., & Wang, L. (2025). Multi-task reinforcement learning and explainable AI-Driven platform for personalized planning and clinical decision support in orthodontic-orthognathic treatment. Scientific Reports, 15(1), 24502. https://doi.org/10.1038/s41598-025-09236-z

Lin, H., Chen, J., Hu, Y., & Li, W. (2024). Embracing technological revolution: A panorama of machine learning in dentistry. Medicina Oral, Patologia Oral y Cirugia Bucal, 29(6), e742–e749. https://doi.org/10.4317/medoral.26679

Lyakhov, P. A., Dolgalev, A. A., Lyakhova, U. A., Muraev, A. A., Zolotayev, K. E., & Semerikov, D. Y. (2022). Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants. Frontiers in Neuroinformatics, 16, 1067040. https://doi.org/10.3389/fninf.2022.1067040

Ma, J., Lou, J., Jiang, B., Ye, H., Yu, W., Chen, X., Zhou, K., & Zheng, Y. (2025). Neural orthodontic staging: Predicting teeth movements with a Transformer. IEEE Transactions on Visualization and Computer Graphics, 31(9), 6253–6267. https://doi.org/10.1109/TVCG.2024.3504866

Ma, Z., Du, C., Lao, Q., & Xie, X. (2025). Generative artificial intelligence: Applications and future prospects in dentistry. International Dental Journal, 76(1), 108276. https://doi.org/10.1016/j.identj.2025.108276

Mahesh Batra, A., & Reche, A. (2023). A New Era of dental care: Harnessing artificial intelligence for better diagnosis and treatment. Cureus, 15(11), e49319. https://doi.org/10.7759/cureus.49319

Mallineni, S. K., Sethi, M., Punugoti, D., Kotha, S. B., Alkhayal, Z., Mubaraki, S., Almotawah, F. N., Kotha, S. L., Sajja, R., Nettam, V., Thakare, A. A., & Sakhamuri, S. (2024). Artificial intelligence in dentistry: A descriptive review. Bioengineering (Basel, Switzerland), 11(12), 1267. https://doi.org/10.3390/bioengineering11121267

Martínez-Valencia, M., Navarro, C., Vázquez-López, J., Hernández-Arellano, J., Jiménez-García, J., & Díaz-León, J. (2022). Optimization of titanium cranial implant designs using generalized reduced gradient method, analysis of finite elements, and artificial neural networks. Revista Internacional de Metodos Numericos Para Calculo y Diseno En Ingenieria, 38(2). https://doi.org/10.23967/j.rimni.2022.06.004

McGrath, C. E., & Bonsor, S. J. (2022). Survival of direct resin composite onlays and indirect tooth-coloured adhesive onlays in posterior teeth: a systematic review. British Dental Journal. https://doi.org/10.1038/s41415-022-4395-3

Moeini, A., & Torabi, S. (2025). The Role of Artificial Intelligence in Dental Diagnosis and Treatment Planning. Journal of Oral and Dental Health Nexus, 2(1), 14-26. https://doi.org/10.61838/kman.jodhn.2.1.2

Mordanov, O., & Khabadze, Z. (2024). Esthetic rehabilitation of the patient with BOPT, veneer, and implant crown and eLAB Prime system in the digital protocol: Case report. Case Reports in Dentistry, 2024(1). https://doi.org/10.1155/2024/4439636

Mun, S. B., Yoo, S. R., Kim, Y. J., Kim, K., Kim, B. C., & Kim, K. G. (2025). AI-driven prediction of dental implant numbers to be placed for patient-specific treatment planning. International Dental Journal, 75(6), 103896. https://doi.org/10.1016/j.identj.2025.103896

Murat, F., Sevinç Gül, S. N., & Şensoy, A. T. (2025). Predictive mathematical modeling of biomechanical behavior in all-on-4 implants design: effects of distal implant and occlusal load angulation using RSM based on FEA. Frontiers in Bioengineering and Biotechnology, 13(1644776), 1644776. https://doi.org/10.3389/fbioe.2025.1644776

Murshida, P. K., Roveena, P. M., Mohammed, I., Raj, A. S., Sharvari, D. J., & Dhayanidhi, A. (2025). AI-assisted treatment planning in orthodontics: Predicting tooth movement. Journal of Clinical and Cosmetic Dentistry, 11(6), 225–229. https://doi.org/10.61336/jccp/25-06-34

Muthukrishnan, N., Maleki, F., Ovens, K., Reinhold, C., Forghani, B., & Forghani, R. (2020). Brief history of artificial intelligence. Neuroimaging Clinics of North America, 30(4), 393–399. https://doi.org/10.1016/j.nic.2020.07.004

Najeeb, M., & Islam, S. (2025). Artificial intelligence (AI) in restorative dentistry: current trends and future prospects. BMC Oral Health, 25(1), 592. https://doi.org/10.1186/s12903-025-05989-1

Okkar Kyaw, Inokoshi, M., & Kanazawa, M. (2024). Tribological aspects of enamel wear caused by zirconia and lithium disilicate: A meta-narrative review. The Japanese Dental Science Review, 60, 258–270. https://doi.org/10.1016/j.jdsr.2024.11.001

Oulmalme, C., Nakouri, H., & Jaafar, F. (2025). A systematic review of generative AI approaches for medical image enhancement: Comparing GANs, transformers, and diffusion models. International Journal of Medical Informatics, 199(105903), 105903. https://doi.org/10.1016/j.ijmedinf.2025.105903

Pable, G., Saha, S. G., Saha, M. K., Agarwal, R. S., Meena, S. S., & Poddar, G. (2025). Comparative evaluation of stress distribution in maxillary premolar restored with onlay fabricated with different restorative materials - A three-dimensional finite element analysis study. Journal of Conservative Dentistry and Endodontics, 28(8), 783–789. https://doi.org/10.4103/JCDE.JCDE_788_24

Prados-Privado, M., Martínez-Martínez, C., Gehrke, S. A., & Prados-Frutos, J. C. (2020). Influence of bone definition and finite element parameters in bone and dental implants stress: A literature review. Biology, 9(8), 224. https://doi.org/10.3390/biology9080224

Preda, F., Elshazly, N., & Jacobs, R. (2025). AI-enhanced orthodontic treatment planning - a scoping review on evidence-based clinical application with commercial software overview. Journal of Dentistry, 163(106112), 106112. https://doi.org/10.1016/j.jdent.2025.106112

Puri, A., & Prathap, M. S. (2025). Biomimetic strategies for the rehabilitation of compromised anterior teeth. Contemporary Clinical Dentistry, 16(3), 218–223. https://doi.org/10.4103/ccd.ccd_231_25

Rathee, M., Divakar, S., Jain, P., Chahal, S., & Singh, S. (2023). Anterior esthetic rehabilitation with full and partial veneers using conventional and digital techniques: A case series. Journal of Conservative Dentistry and Endodontics, 26(5), 601–607. https://doi.org/10.4103/jcd.jcd_157_23

Ray, T. R., Kellogg, R. T., Fargen, K. M., Hui, F., & Vargas, J. (2023). The perils and promises of generative artificial intelligence in neurointerventional surgery. Journal of Neurointerventional Surgery, 16(1), 4–7. https://doi.org/10.1136/jnis-2023-020353

Rees, J. S., & Jacobsen, P. H. (1993). The elastic moduli of enamel and dentine. Clinical Materials, 14(1), 35–39. https://doi.org/10.1016/0267-6605(93)90045-9

Revilla-León, M., Gómez-Polo, M., Vyas, S., Barmak, B. A., Galluci, G. O., Att, W., & Krishnamurthy, V. R. (2023). Artificial intelligence applications in implant dentistry: A systematic review. The Journal of Prosthetic Dentistry, 129(2), 293–300. https://doi.org/10.1016/j.prosdent.2021.05.008

Roongruangsilp, P., Narkbuakaew, W., & Khongkhunthian, P. (2025). Performance of two different artificial intelligence models in dental implant planning among four different implant planning software: a comparative study. BMC Oral Health, 25(1), 984. https://doi.org/10.1186/s12903-025-06336-0

Saini, H., Ackland, D. C., Gong, L., Cheng, & Röhrle, O. (2020). Occlusal load modelling significantly impacts the predicted tooth stress response during biting: a simulation study. Computer Methods in Biomechanics and Biomedical Engineering, 23(7), 261–270. https://doi.org/10.1080/10255842.2020.1711886

Satapathy, S. K., Kunam, A., Rashme, R., Sudarsanam, P. P., Gupta, A., & Kumar, H. S. K. (2024). AI-assisted treatment planning for dental implant placement: Clinical vs AI-generated plans. Journal of Pharmacy & Bioallied Sciences, 16(Suppl 1), S939–S941. https://doi.org/10.4103/jpbs.jpbs_1121_23

Sayed, A. J., Mohsin, S. F., Garg, K. K., Agwan, M. A. S., Tareen, S. U. K., Alruthea, M. S., & Mohan, S. (2025). Finite Element Analysis (FEA) for influence of variation in Dental Implant dimensions (length and diameter) on Peri-implant Bone Stress/strain distribution: A systematic review. Pakistan Journal of Medical Sciences Quarterly, 41(1), 318–330. https://doi.org/10.12669/pjms.41.1.8991

Sciarra, F. M., Caivano, G., Cacioppo, A., Messina, P., Cumbo, E. M., Di Vita, E., & Scardina, G. A. (2025). Dentistry in the era of artificial intelligence: Medical behavior and clinical responsibility. Prosthesis, 7(4), 95. https://doi.org/10.3390/prosthesis7040095

Shujaat, S. (2025). Automated machine learning in dentistry: A narrative review of applications, challenges, and future directions. Diagnostics (Basel, Switzerland), 15(3), 273. https://doi.org/10.3390/diagnostics15030273

Siluvai, S., Narayanan, V., Ramachandran, V. S., & Lazar, V. R. (2025). Generative pre-trained transformer: Trends, applications, strengths and challenges in dentistry: A systematic review. Healthcare Informatics Research, 31(2), 189–199. https://doi.org/10.4258/hir.2025.31.2.189

Ustrell-Barral, M., Zamora-Olave, C., Khoury-Ribas, L., Rovira-Lastra, B., & Martinez-Gomis, J. (2024). Reliability, reference values and factors related to maximum bite force measured by the Innobyte system in healthy adults with natural dentitions. Clinical Oral Investigations, 28(11), 620. https://doi.org/10.1007/s00784-024-06014-5

Villena, F., Véliz, C., García-Huidobro, R., & Aguayo, S. (2025). Generative artificial intelligence in dentistry: A narrative review of current approaches and future challenges. Dentistry Review, 5(4), 100160. https://doi.org/10.1016/j.dentre.2025.100160

Vivekananthan, S. (2024). Comparative analysis of generative models: Enhancing image synthesis with VAEs, GANs, and Stable Diffusion. In arXiv [cs.CV]. https://doi.org/10.48550/ARXIV.2408.08751

Wang, C., Zhu, X., Hong, J. C., & Zheng, D. (2019). Artificial intelligence in radiotherapy treatment planning: Present and future. Technology in Cancer Research & Treatment, 18, 1533033819873922. https://doi.org/10.1177/1533033819873922

Wang, J.-Q., Zhang, Y., Pang, M., Wang, Y.-Q., Yuan, J., Peng, H., Zhang, W., Dai, L., & Li, H.-W. (2022). Biomechanical comparison of six different root-analog implants and the conventional Morse taper implant by finite element analysis. Frontiers in Genetics, 13, 915679. https://doi.org/10.3389/fgene.2022.915679

Wang, L., Xu, Y., Wang, W., & Lu, Y. (2025). Application of machine learning in dentistry: insights, prospects and challenges. Acta Odontologica Scandinavica, 84, 145–154. https://doi.org/10.2340/aos.v84.43345

Wang, Y.-C. C., Chen, T.-L., Vinayahalingam, S., Wu, T.-H., Chang, C. W., Chang, H. H., Wei, H.-J., Chen, M.-H., Ko, C.-C., Moin, D. A., van Ginneken, B., Xi, T., Tsai, H.-C., Chen, M.-H., Hsu, T.-M. H., & Chou, H. (2025). Artificial intelligence to assess dental findings from panoramic radiographs -- A multinational study. In arXiv [cs.CV]. https://doi.org/10.48550/ARXIV.2502.10277

Xie, B., Zhang, L., Wang, Y., Chu, Y., & Lu, Y. (2025). Finite element analysis in the Dental Sciences: A Bibliometric and a Visual Study. International Dental Journal, 75(2), 855–867. https://doi.org/10.1016/j.identj.2024.08.005

Xie, B.-Y., He, X., Hu, L., Guo, S.-L., Chen, J.-L., Zhang, J., Shen, X.-Q., Geng, Y.-M., & Li, W. (2025). Morphological comparison between artificial intelligence-driven and manual CAD design in single tooth restoration: a preliminary study. BMC Oral Health, 25(1), 1633. https://doi.org/10.1186/s12903-025-07004-z

Yang, G., Shen, H., Jang, Y., & Cheng, X. (2025). Finite element analysis-assisted surgical planning and evaluation of flap design in hand surgery. Frontiers in Bioengineering and Biotechnology, 13, 1611993. https://doi.org/10.3389/fbioe.2025.1611993

Yli-Urpo, T., Lassila, L., Närhi, T., & Vallittu, P. (2025). Cement layer thickness and load-bearing capacity of tooth restored with lithium-disilicate glass ceramic and hybrid ceramic occlusal veneers. Dental Materials: Official Publication of the Academy of Dental Materials, 41(2), 212–219. https://doi.org/10.1016/j.dental.2024.11.004

Zannah, R., Bashar, M., Mushfiq, R. B., Chakrabarty, A., Hossain, S., & Jung, Y. J. (2024). Semantic segmentation on panoramic dental X-ray images using U-net architectures. IEEE Access: Practical Innovations, Open Solutions, 12, 44598–44612. https://doi.org/10.1109/access.2024.3380027

Zarone, F., Di Mauro, M. I., Ausiello, P., Ruggiero, G., & Sorrentino, R. (2019). Current status on lithium disilicate and zirconia: a narrative review. BMC Oral Health, 19(1), 134. https://doi.org/10.1186/s12903-019-0838-x

Zhu, Y., Hu, J., Luo, B., Yuan, Y., & Jiang, Q. (2025). Biomechanical considerations in RPD design: application and perspective of finite element method in distal extension removable partial denture rehabilitation. Frontiers in Dental Medicine, 6(1667504), 1667504. https://doi.org/10.3389/fdmed.2025.1667504