ADVANCED TECHNOLOGIES IN ORAL AND MAXILLOFACIAL SURGERY

MaxillofacialSurgerydakapak

CHALLENGES AND LIMITATIONS IN IMPLEMENTING ARTIFICIAL INTELLIGENCE (AI) TECHNOLOGIES IN CLINICAL PRACTICE

Ömer Orkun Cevizcioğlu

Topraklık Oral and Dental Health Center, Department of Oral and Maxillofacial Surgery,Ankara, Türkiye

Cevizcioğlu ÖO. Challenges and Limitations in Implementing Artificial Intelligence (AI) Technologies in Clinical Practice. Karasu HA, ed. Advanced Technologies in Oral and Maxillofacial Surgery. 1st ed. Ankara: Türkiye Klinikleri; 2025. p.111119.

ABSTRACT

Owing to significant advancements in computational efficiency and data processing capacities within the industry, artificial intelligence (AI) has gained prominence across various societal sectors, includ ing dentistry. AI, encompassing learning, decisionmaking, and problemsolving, is a domain within computer science dedicated to developing systems that can execute activities typically necessitating human intelligence.

AI applications possess considerable potential in the domain of oral and maxillofacial surgery (OMS). Trained algorithms offer surgeons remarkable precision and control during intricate surgeries and may transform OMS by enhancing human performance in activities like image and speech recognition.

Detailed imaging systems such as computed tomography and magnetic resonance imaging are very important in surgical operation planning, during the operation and in the evaluation of the results. AIassisted navigation systems can assist in accurately positioning surgical instruments, implants or other materials used by providing simultaneous guidance to surgeons during the operation. These sys tems can reduce operation time, enable more precise operation, minimise tissue damage and ultimately increase surgical success. By analysing medical data with precision, AI can identify anomalies and provide early diagnosis of diseases. Thanks to the ability to perform daily tasks faster, AI can reduce the physician’s workload, facilitate diagnoses and decisionmaking. Thanks to AI technologies, infor mation exchange between surgeons, radiologists, orthodontists and other healthcare professionals can be facilitated and multidisciplinary cooperation can be improved day by day.

It seems more likely that we will see the development of AI in healthcare in terms of enabling clinicians to save time spent performing certain timeconsuming and repetitive tasks. However, criticisms of these technologies will need to continue in order to avoid the clinician’s willingness to unquestioningly accept diagnostic and treatment decisions made by AI.

This section examines the key AI challenges associated with each area of OMS practice, highlighting common clinical practices, standard algorithms and specific constraints.

Keywords: Artificial intelligence; Deep learning; Machine learning; Oral surgery; Maxillofacial surgery; Challenges of AI; Limitations of AI; Black box

Referanslar

  1. Rasteau S, Ernenwein D, Savoldelli C, Bouletreau P. Artificial intelligence for oral and maxillo-facial surgery: a narrative review. J Stomatol Oral Maxillofac Surg 2022;123:276-82. [Crossref]  [PubMed]
  2. Sillmann YM, Monteiro JLGC, Eber P, Baggio AMP, Peacock ZS, Guastaldi FPS. Empowering surgeons: will artificial intelligence change oral and maxillofacial surgery? Int J Oral Maxillofac Surg. 2025 Feb;54(2):179-190. [Crossref]  [PubMed]
  3. Rokhshad R, Keyhan SO, Yousefi P. Artificial intelligence applications and ethical challenges in oral and maxillo- facial cosmetic surgery: a narrative re view. Maxillofac Plast Reconstr Surg 2023;45:14. [Crossref]  [PubMed]  [PMC]
  4. Miragall MF, Knoedler S, Kauke-Navarro M, Saadoun R, Grabenhorst A, Grill FD, Ritschl LM, Fichter AM, Safi AF, Knoedler L. Face the future-artificial in telligence in oral and maxillofacial surgery. J Clin Med 2023;12:6843. [Crossref]  [PubMed]  [PMC]
  5. Reddy K, Gharde P, Tayade H, Patil M, Reddy LS, Surya D. Advancements in robotic surgery: a comprehensive over view of current utilizations and up coming frontiers. Cureus 2023; 15:e50415. [Crossref]
  6. Warin K, Limprasert W, Suebnukarn S, Jinaporntham S, Jantana P. Performance of deep convolutional neural network for classification and detection of oral potentially malignant disorders in photographic images. Int J Oral Maxillofac Surg 2022;51:699-704. [Crossref]  [PubMed]
  7. Kenner B, Chari ST, Kelsen D, Klimstra DS, Pandol SJ, Rosenthal M, et al. Artificial in telligence and early detection of pan creatic cancer: 2020 summative review. Pancreas 2021;50:251-79. [Crossref]  [PubMed]  [PMC]
  8. Ali O, Abdelbaki W, Shrestha A, Elbasi E, Alryalat MAA, Dwivedi YK. A sys tematic literature review of artificial in telligence in the healthcare sector: benefits, challenges, methodologies, and functionalities. J Innov Knowl 2023; 8:100333. [Crossref]
  9. Sussillo D, Barak O. Opening the black box: low-dimensional dynamics in high- dimensional recurrent neural networks. Neural Comput 2013;25:626-49. [Crossref]  [PubMed]
  10. Sturm I, Lapuschkin S, Samek W, Müller KR. Interpretable deep neural networks for single-trial EEG classifica tion. J Neurosci Methods 2016; 274:141-5. [Crossref]  [PubMed]
  11. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg 2018;268:70-6. [Crossref]  [PubMed]  [PMC]
  12. Ariji Y, Fukuda M, Kise Y, Nozawa M, Yanashita Y, Fujita H, Katsumata A, Ariji E. Contrast-enhanced computed tomography image assessment of cer vical lymph node metastasis in patients with oral cancer by using a deep learning systemof artificial intelligence. Oral Surg Oral Med Oral Pathol Oral Radiol 2019;127:458-63. [Crossref]  [PubMed]
  13. Ariji Y, Kise Y, Fukuda M, Kuwada C, Ariji E. Segmentation of metastatic cer vical lymph nodes from CT images of oral cancers using deep-learning tech nology. Dentomaxillofac Radiol 2022;51:20210515. [Crossref]  [PubMed]  [PMC]
  14. Gürses BO, Alpoz E, Şener M, Çankaya H, Boyacıoğlu H, Güneri P. A support vector machine-based algorithm to identify bisphosphonate-related osteo necrosis throughout the mandibular bone by using cone beam computerized tomography images. Dentomaxillofac Radiol 2023;52:20220390. [Crossref]  [PubMed]  [PMC]
  15. Han Q, Du L, Mo Y, Huang C, Yuan Q. Machine learning based non-enhanced CT radiomics for the identification of orbital cavernous venous malforma tions: an innovative tool. J Craniofac Surg 2022;33:814-20. [Crossref]  [PubMed]
  16. Lee HS, Yang S, Han JY, Kang JH, Kim JE, Huh KH, et al. Automatic detection and clas sification of nasopalatine duct cyst and periapical cyst on panoramic radio graphs using deep convolutional neural networks. Oral Surg Oral Med Oral Pathol Oral Radiol 2024;138:184-95. [Crossref]  [PubMed]
  17. Bekedam NM, Idzerda LHW, van Alphen MJA, van Veen RLP, Karssemakers LHE, Karakullukcu MB, Smeele LE. Implementing a deep learning model for automatic tongue tumour segmentation in ex-vivo 3-di mensional ultrasound volumes. Br J Oral Maxillofac Surg 2024;62:284-9. [Crossref]  [PubMed]
  18. Lee A, Park GC, Cho ES, Choi YJ, Jeon KJ, Han SS, Lee C. Radiomics-based sialadenitis staging in contrast-enhanced computed tomography and ultra sonography: a preliminary rat model study. Oral Surg Oral Med Oral Pathol Oral Radiol 2023;136:231-9. [Crossref]  [PubMed]
  19. Kwon O, Yong TH, Kang SR, Kim JE, Huh KH, Heo MS, et al. Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network. Dentomaxillofac Radiol 2020;49:20200185. [Crossref]  [PubMed]  [PMC]
  20. Deng HH, Liu Q, Chen A, Kuang T, Yuan P, Gateno J, et al. Clinical feasibility of deep learning-based automatic head CBCT image segmentation and land mark detection in computer-aided sur gical simulation for orthognathic surgery. Int J Oral Maxillofac Surg 2023; 52:793-800. [Crossref]  [PubMed]  [PMC]
  21. Gunson MJ, Arnett GW. Orthognathic virtual treatment planning for functional esthetic results. Semin Orthod 2019; 25:230-47. [Crossref]
  22. Lim HK, Choi YJ, Song IS, Lee JH. Retrospective evaluation of the clinical utility of reconstructed computed to mography images using artificial in telligence in the oral and maxillofacial region. J Craniomaxillofac Surg 2023; 51:543-50. [Crossref]  [PubMed]
  23. Fontenele RC, Gerhardt MDN, Picoli FF, Van Gerven A, Nomidis S, Willems H, et al. Convolutional neural network-based automated max illary alveolar bone segmentation on cone-beam computed tomography images. Clin Oral Implants Res 2023; 34:565-74. [Crossref]  [PubMed]
  24. Hong W, Kim SM, Choi J, Ahn J, Paeng JY, Kim H. Automated cephalometric landmark detection using deep re inforcement learning. J Craniofac Surg 2023;34:2336-42. [Crossref]
  25. Morita D, Mazen S, Tsujiko S, Otake Y, Sato Y, Numajiri T. Deep-learning- based automatic facial bone segmenta tion using a two-dimensional U-Net. Int J Oral Maxillofac Surg 2023;52:787-92. [Crossref]  [PubMed]
  26. Park JA, Kim D, Yang S, Kang JH, Kim JE, Huh KH, et al. Automatic detection of posterior superior alveolar artery in dental cone- beam CT images using a deeply su pervised multi-scale 3D network. Dentomaxillofac Radiol 2024;53:22-31. [Crossref]  [PubMed]  [PMC]
  27. Warin K, Limprasert W, Suebnukarn S, Inglam S, Jantana P, Vicharueang S. Assessment of deep convolutional neural network models for mandibular fracture detection in panoramic radiographs. Int J Oral Maxillofac Surg 2022;51:1488-94. [Crossref]  [PubMed]
  28. Wang X, Xu Z, Tong Y, Xia L, Jie B, Ding P, et al. Detection and classification of man dibular fracture on CT scan using deep convolutional neural network. Clin Oral Invest 2022;26:4593-601. [Crossref]  [PubMed]
  29. Lin B, Cheng M, Wang S, Li F, Zhou Q. Automatic detection of anteriorly dis placed temporomandibular joint discs on magnetic resonance images using a deep learning algorithm. Dentomaxillofac Radiol 2022;51:20210341. [Crossref]  [PubMed]  [PMC]
  30. Yoshimi Y, Mine Y, Ito S, Takeda S, Okazaki S, Nakamoto T, et al. Image preprocessing with contrast- limited adaptive histogram equalization improves the segmentation performance of deep learning for the articular disk of the temporomandibular joint on mag netic resonance images. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 138:128-41. [Crossref]  [PubMed]
  31. Nozawa M, Ito H, Ariji Y, Fukuda M, Igarashi C, Nishiyama M, et al. E. Automatic segmentation of the tempor omandibular joint disc on magnetic re sonance images using a deep learning technique. Dentomaxillofac Radiol 2022;51:20210185. [Crossref]  [PubMed]  [PMC]
  32. Iwasaki H. Bayesian belief network analysis applied to determine the pro gression of temporomandibular dis orders using MRI. Dentomaxillofac Radiol 2015;44:20140279. [Crossref]  [PubMed]  [PMC]
  33. Kim JY, Kahm SH, Yoo S, Bae SM, Kang JE, Lee SH. The efficacy of su pervised learning and semi-supervised learning in diagnosis of impacted third molar on panoramic radiographs through artificial intelligence model. Dentomaxillofac Radiol 2023; 52:20230030. [Crossref]  [PubMed]  [PMC]
  34. Kuwada C, Ariji Y, Fukuda M, Kise Y, Fujita H, Katsumata A, et al. Deep learning systems for detecting and clas sifying the presence of impacted super numerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol 2020;130:464-9. [Crossref]  [PubMed]
  35. Fukuda M, Ariji Y, Kise Y, Nozawa M, Kuwada C, Funakoshi T, Muramatsu C, Fujita H, Katsumata A, Ariji E. Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol 2020;130:336-43. [Crossref]  [PubMed]
  36. Kong HJ, Eom SH, Yoo JY, Lee JH. Identification of 130 dental implant types using ensemble deep learning. Int J Oral Maxillofac Implants 2023;38:150-6. [Crossref]  [PubMed]
  37. Mohammad-Rahimi H, Motamedian SR, Pirayesh Z, Haiat A, Zahedrozegar S, Mahmoudinia E, Rohban MH, Krois J, Lee JH, Schwendicke F. Deep learning in periodontology and oral implantology: a scoping review. J Periodontal Res 2022;57:942-51. [Crossref]  [PubMed]
  38. da Mata Santos RP, Vieira Oliveira Prado HE, Soares Aranha Neto I, Alves de Oliveira GA, Vespasiano Silva AI, Zenóbio EG, et al. Automated identification of dental implants using artificial intelligence. Int J Oral Maxillofac Implants 2021;36:918-23. [Crossref]  [PubMed]
  39. Hadj Saïd M, Le Roux MK, Catherine JH, Lan R. Development of an artificial intelligence model to identify a dental implant from a radiograph. Int J Oral Maxillofac Implants 2020;35:1077-82. [Crossref]  [PubMed]
  40. Arjmand H, Clement A, Hardisty M, Fialkov JA, Whyne CM. Artificial intelligence-based modeling can predict face shape based on underlying craniomaxillofacial bone. J Craniofac Surg 2023;34:1915-21. [Crossref]  [PubMed]
  41. Geisler EL, Agarwal S, Hallac RR, Daescu O, Kane AA. A role for artificial intelligence in the classification of craniofacial anomalies. J Craniofac Surg 2021;32:967-9. [Crossref]  [PubMed]
  42. Xu M, Liu B, Luo Z, Ma H, Sun M, Wang Y, et al. Using a new deep learning method for 3D cephalometry in patients with cleft lip and palate. J Craniofac Surg 2023;34:1485-8. [Crossref]
  43. Han W, Xia W, Zhang Z, Kim BS, Chen X, Yan Y, Sun M, Lin L, Xu H, Chai G, Wang L. Radiomics and artificial intelligence study of masseter muscle segmentation in patients with hemifacial microsomia. J Craniofac Surg 2023; 34:809-12. [Crossref]  [PubMed]
  44. Mizutani K, Miwa T, Sakamoto Y, Toda M. Application of deep learning techniques for automated diagnosis of non-syndromic craniosynostosis using skull. J Craniofac Surg 2022;33:1843-6. [Crossref]  [PubMed]
  45. Blum JD, Beiriger J, Villavisanis DF, Morales C, Cho DY, Tao W, et al. Machine learning in metopic craniosynostosis: does phenotypic severity predict long-term esthetic outcome? J Craniofac Surg 2023;34:58-64. [Crossref]  [PubMed]  [PMC]
  46. Woo SH, Kim YC, Kim J, Kwon S, Oh TS. Artificial intelligence-based numerical analysis of the quality of facial reanimation: a comparative retrospective cohort study between one-stage dual innervation and single innervation. J Craniomaxillofac Surg 2023;51:265-71. [Crossref]  [PubMed]
  47. Ding M, Kang Y, Yuan Z, Shan X, Cai Z. Detection of facial landmarks by a convolutional neural network in patients with oral and maxillofacial disease. Int J Oral Maxillofac Surg 2021; 50:1443-9. [Crossref]  [PubMed]
  48. Rasteau, S, Ernenwein D, Savoldelli, C, Bouletreau P. Artificial intelligence for oral and maxillo-facial surgery: A narrative review. Journal of stomatology, oral and maxillofacial surgery, 2022 123(3),276-282. [Crossref]  [PubMed]
  49. Neri E, Coppola F, Miele V, Bibbolino C, Grassi R. Artificial intelligence: Who is responsible for the diagnosis? Radiol Med (Torino) 2020;125:517-21. [Crossref]  [PubMed]
  50. Langlotz CP. Will Artificial Intelligence Replace Radiologists? Radiol Artif Intell 2019;1:e190058. [Crossref]  [PubMed]  [PMC]
  51. Mazurowski MA. Artificial Intelligence May Cause a Significant Disruption to the Radiology Workforce. J Am Coll Radiol JACR 2019;16:1077-82. [Crossref]  [PubMed]
  52. Bernier GV, Sanchez JE. Surgical simulation: the value of individualization. Surg Endosc. 2016;30(8):3191-7. [Crossref]  [PubMed]
  53. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30-6. [Crossref]  [PubMed]  [PMC]
  54. Park JJ, Tiefenbach J, Demetriades AK. The role of artificial intelligence in surgical simulation. Front. Med. Technol. 2022;4:1076755. [Crossref]  [PubMed]  [PMC]
  55. Mu Y, He D. The Potential Applications and Challenges of ChatGPT in the Medical Field, International Journal of General Medicine 2024;17: 817-826. [Crossref]  [PubMed]  [PMC]