ADVANCED TECHNOLOGIES IN ORAL AND MAXILLOFACIAL SURGERY

MaxillofacialSurgerydakapak

ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING:ARTIFICIAL INTELLIGENCE (AI)DRIVEN INNOVATIONS IN HEALTHCARE AND DENTISTRY

Mahide Büşra Başkan

İstanbul University, Faculty of Dentistry, Department of Oral and Maxillofacial Surgery, İstanbul, Türkiye

Başkan MB. Artificial Intelligence (AI) and Machine Learning: Artificial Intelligence (AI)Driven Innovations in Healthcare and Dentistry. Karasu, HA ed. Advanced Technologies in Oral and Maxillofacial Surgery. 1st ed. Ankara: Türkiye Klinikleri; 2025. p.121131.

ABSTRACT

Artificial intelligence (AI) refers to the development of intelligent systems that can perform tasks per formed by humans. Machine learning is a subfield of AI in which algorithms are used to learn statistical patterns and structures in data, giving it the ability to make predictions on new data. Today, AI is an important branch of engineering that applies innovative solutions and new concepts to solve complex problems. In recent years, it has had a wide impact on daily life with its contributions to different sectors and has demonstrated its potential to offer a smarter and more comfortable life in the future. AI has led to significant transformations in healthcare by using big data sets. It aims to optimize processes through clinical decision support systems while easing the workload of healthcare professionals. In the healthcare sector, AI is widely used in many areas such as patient followup and management, prognostic assessment, medical image analysis, prediction of genetic diseases, drug discovery and development, robotic surgery, wearable devices, hospital management and telehealth services. These innovations are expected to improve the quality of patient care, increase diagnostic accuracy and oper ational efficiency in healthcare services. Thanks to digitalization, dentistry has become one of the most suitable healthcare disciplines for AI applications. The regular use of digital imaging and electronic health records facilitates the integration of AI into dental practice. AI reduces the workload of dentists in diagnosis, decisionmaking and treatment planning, increases accuracy and precision, and improves clinical decisionmaking processes with the support of data analytics. In dentistry, AI applications have become widespread in branches such as endodontics, pedodontics, implantology, periodontology, oral and maxillofacial surgery, orthodontics, restorative and prosthetic dental treatments, oral pathology, oral and maxillofacial radiology and forensic dentistry. In addition, AI technologies are utilized to teach clinical skills to dental students with virtual reality supported systems in educational processes. Just as the internet has become indispensable in daily life, it is predicted that AI will become an integral part of health and dentistry practices in the near future. By becoming an integral part of healthcare services, AI is expected to increase clinical effectiveness and contribute to patient satisfaction. It is important for physicians to be open to innovations by adopting this technology instead of being afraid of AI and to utilize these developments within an ethical framework.

Keywords: Artificial intelligence; Delivery of health care; Dentistry; Machine learning

Referanslar

  1. Chalmers D, MacKenzie NG, Carter S. Artificial intelligence and entrepreneurship: Implications for venture creation in the fourth industrial revolution. Entrepreneurship Theory and Practice. 2021;45(5):1028-53. [Crossref]
  2. Turing A M. Computing machinery and intelligence. In: Parsing the Turing Test. Dordrecht: Springer, 2009. pp. 23-65. [Crossref]
  3. Moor J. The Dartmouth College artificial intelligence conference: The next fifty years. AI Magazine. 2006;27(4):87-. [Crossref]
  4. Schwendicke F, Samek W, Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res. 2020;99(7):769-774. [Crossref]  [PubMed]  [PMC]
  5. Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eDoctor: machine learning and the future of medicine. J Intern Med. 2018;284(6):603-619. [Crossref]  [PubMed]
  6. Bichu YM, Hansa I, Bichu AY, Premjani P, Flores-Mir C, Vaid NR. Applications of artificial intelligence and machine learning in orthodontics: a scoping review. Prog Orthod. 2021;22(1):18. [Crossref]  [PubMed]  [PMC]
  7. Vishwanathaiah S, Fageeh HN, Khanagar SB, Maganur PC. Artificial Intelligence Its Uses and Application in Pediatric Dentistry: A Review. Biomedicines. 2023;11(3):788. [Crossref]  [PubMed]  [PMC]
  8. Kufel J, Bargieł-Łączek K, Kocot S, et al. What Is Machine Learning, Artificial Neural Networks and Deep Learning?-Examples of Practical Applications in Medicine. Diagnostics (Basel). 2023;13(15):2582. [Crossref]  [PubMed]  [PMC]
  9. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69S:36-40. [Crossref]  [PubMed]
  10. Pannu A. Artificial intelligence and its application in different areas. International Journal of Engineering and Innovative Technology (IJEIT). 2015;4(10):79-84. [Link]
  11. Fiske A, Henningsen P, Buyx A. Your Robot Therapist Will See You Now: Ethical Implications of Embodied Artificial Intelligence in Psychiatry, Psychology, and Psychotherapy. J Med Internet Res. 2019;21(5):e13216. [Crossref]  [PubMed]  [PMC]
  12. Abdelkareem MA, Soudan B, Mahmoud MS, Sayed ET, AlMallahi MN, Inayat A, et al. Progress of artificial neural networks applications in hydrogen production. Chemical Engineering Research and Design. 2022;182:66-86. [Crossref]
  13. Panesar A. Machine learning and AI for healthcare: Big Data for Improved Health Outcomes. 2nd edition, Coventry, UK. Springer; 2019. [Crossref]
  14. Salameh T, Kumar PP, Olabi A, Obaideen K, Sayed ET, Maghrabie HM, et al. Best battery storage technologies of solar photovoltaic systems for desalination plant using the results of multi optimization algorithms and sustainable development goals. Journal of Energy Storage. 2022;55:105312. [Crossref]
  15. Rahman MJ, Morshed BI, Harmon B, Rahman M. A pilot study towards a smart-health framework to collect and analyze biomarkers with low-cost and flexible wearables. Smart Health. 2022;23:100249. [Crossref]
  16. Shukla Shubhendu S, Vijay J. Applicability of artificial intelligence in different fields of life. International Journal of Scientific Engineering and Research. 2013;1(1):28-35. [Crossref]  [PubMed]
  17. Denecke K, Baudoin CR. A Review of Artificial Intelligence and Robotics in Transformed Health Ecosystems. Front Med (Lausanne). 2022;9:795957. Published 2022 Jul 6. [Crossref]  [PubMed]  [PMC]
  18. Kickbusch I, Agrawal A, Jack A, Lee N, Horton R. Governing health futures 2030: growing up in a digital world-a joint The Lancet and Financial Times Commission. Lancet. 2019;394(10206):1309. [Crossref]  [PubMed]
  19. Kickbusch I, Piselli D, Agrawal A, et al. The Lancet and Financial Times Commission on governing health futures 2030: growing up in a digital world. Lancet. 2021;398(10312):1727-1776. [Crossref]  [PubMed]
  20. Khan B, Fatima H, Qureshi A, et al. Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector. Biomed Mater Devices. Published online February 8, 2023. [Crossref]  [PubMed]  [PMC]
  21. Aldwean A, Tenney D. Artificial Intelligence in Healthcare Sector: A Literature Review of the Adoption Challenges. Open Journal of Business and Management. 2024;12(01):129-47. [Crossref]
  22. Hoşgör H, Güngördü H. Sağlıkta Yapay Zekanın Kullanım Alanları Üzerine Nitel Bir Araştırma. Avrupa Bilim ve Teknoloji Dergisi. 2022(35):395-407. [Crossref]
  23. AlNuaimi D, AlKetbi R. The role of artificial intelligence in plain chest radiographs interpretation during the Covid-19 pandemic. BJR Open. 2022;4(1):20210075. Published 2022 May 26. [Crossref]
  24. Dicle O. Artificial intelligence in diagnostic ultrasonography. Diagn Interv Radiol. 2023;29(1):40-45. [Crossref]  [PubMed]  [PMC]
  25. Niiya A, Murakami K, Kobayashi R, et al. Development of an artificial intelligence-assisted computed tomography diagnosis technology for rib fracture and evaluation of its clinical usefulness. Sci Rep. 2022;12(1):8363. Published 2022 May 19. [Crossref]  [PubMed]  [PMC]
  26. Cau R, Flanders A, Mannelli L, et al. Artificial intelligence in computed tomography plaque characterization: A review [published correction appears in Eur J Radiol. 2022 Feb;147:110132. Eur J Radiol. 2021;140:109767. [Crossref]  [PubMed]
  27. Karthiga R, Narasimhan K, Amirtharajan R. Diagnosis of breast cancer for modern mammography using artificial intelligence. Mathematics and Computers in Simulation. 2022;202:316-30. [Crossref]
  28. Sechopoulos I, Teuwen J, Mann R. Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: State of the art. Semin Cancer Biol. 2021;72:214-225. [Crossref]  [PubMed]
  29. Rauschecker AM, Rudie JD, Xie L, et al. Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI. Radiology. 2020;295(3):626-637. [Crossref]  [PubMed]  [PMC]
  30. Fritz B, Fritz J. Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches. Skeletal Radiol. 2022;51(2):315-329. [Crossref]  [PubMed]  [PMC]
  31. Martínez-Sellés M, Marina-Breysse M. Current and Future Use of Artificial Intelligence in Electrocardiography. J Cardiovasc Dev Dis. 2023;10(4):175. Published 2023 Apr 17. [Crossref]  [PubMed]  [PMC]
  32. Alsharqi M, Woodward WJ, Mumith JA, Markham DC, Upton R, Leeson P. Artificial intelligence and echocardiography. Echo Res Pract. 2018;5(4):R115-R125. [Crossref]  [PubMed]  [PMC]
  33. Försch S, Klauschen F, Hufnagl P, Roth W. Artificial Intelligence in Pathology. Dtsch Arztebl Int. 2021;118(12):194- 204. [Crossref]  [PubMed]  [PMC]
  34. Germer S, Rudolph C, Labohm L, et al. Survival analysis for lung cancer patients: A comparison of Cox regression and machine learning models. Int J Med Inform. 2024;191:105607. [Crossref]  [PubMed]
  35. Sandeman K, Blom S, Koponen V, et al. AI Model for Prostate Biopsies Predicts Cancer Survival. Diagnostics (Basel). 2022;12(5):1031. Published 2022 Apr 20. [Crossref]  [PubMed]  [PMC]
  36. Somyanonthanakul R, Warin K, Chaowchuen S, Jinaporntham S, Panichkitkosolkul W, Suebnukarn S. Survival estimation of oral cancer using fuzzy deep learning. BMC Oral Health. 2024;24(1):519. Published 2024 May 2. [Crossref]  [PubMed]  [PMC]
  37. Tang R, Zhang S, Ding C, Zhu M, Gao Y. Artificial Intelligence in Intensive Care Medicine: Bibliometric Analysis. J Med Internet Res. 2022;24(11):e42185. Published 2022 Nov 30. [Crossref]  [PubMed]  [PMC]
  38. Mak KK, Wong YH, Pichika MR. Artificial Inteligence in Drug Discovery and Development. In: Hock, F.J., Pugsley, M.K. (eds) Drug Discovery and Evaluation: Safety and Pharmacokinetic Assays. Springer, Cham. 2024:1461-98. [Crossref]
  39. Chaplot N, Pandey D, Kumar Y, Sisodia PS. A comprehensive analysis of artificial intelligence techniques for the prediction and prognosis of genetic disorders using various gene disorders. Archives of Computational Methods in Engineering. 2023;30(5):3301-23. [Crossref]
  40. Iftikhar M, Saqib M, Zareen M, Mumtaz H. Artificial intelligence: revolutionizing robotic surgery: review. Ann Med Surg (Lond). 2024;86(9):5401-5409. Published 2024 Aug 1. [Crossref]  [PubMed]  [PMC]
  41. LaBoone PA, Marques O. Overview of the future impact of wearables and artificial intelligence in healthcare workflows and technology. International Journal of Information Management Data Insights. 2024;4(2):100294. [Crossref]
  42. Mi D, Li Y, Zhang K, Huang C, Shan W, Zhang J. Exploring intelligent hospital management mode based on artificial intelligence. Frontiers in Public Health. 2023;11:1182329. [Crossref]  [PubMed]  [PMC]
  43. Bhagat SV, Kanyal D. Navigating the Future: The Transformative Impact of Artificial Intelligence on Hospital Management- A Comprehensive Review. Cureus. 2024;16(2):e54518. Published 2024 Feb 20. [Crossref]
  44. Amjad A, Kordel P, Fernandes G. A Review on Innovation in Healthcare Sector (Telehealth) through Artificial Intelligence. Sustainability. 2023; 15(8):6655. [Crossref]
  45. Riccardi G, editor Towards healthcare personal agents. Proceedings of the 2014 workshop on roadmapping the future of multimodal interaction research including business opportunities and challenges Published 2014 Nov 16. [Crossref]
  46. Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. 2020;14(4):337-339. [Crossref]  [PubMed]  [PMC]
  47. Pradhan K, John P, Sandhu N. Use of artificial intelligence in healthcare delivery in India. Journal of Hospital Management and Health Policy. 2021;5:28. [Crossref]
  48. Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. Artificial Intelligence in healthcare: Elsevier; 2020;25-60. [Crossref]  [PMC]
  49. Yüzbaşıoğlu E. Attitudes and perceptions of dental students towards artificial intelligence. J Dent Educ. 2021;85(1):60-68. [Crossref]  [PubMed]
  50. Shan T, Tay FR, Gu L. Application of Artificial Intelligence in Dentistry. J Dent Res. 2021;100(3):232-244. [Crossref]  [PubMed]
  51. Mallineni SK, Sethi M, Punugoti D, et al. Artificial Intelligence in Dentistry: A Descriptive Review. Bioengineering (Basel). 2024;11(12):1267. [Crossref]  [PubMed]  [PMC]
  52. Huqh MZU, Abdullah JY, Wong LS, et al. Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate-A Systematic Review. Int J Environ Res Public Health. 2022;19(17):10860. [Crossref]  [PubMed]  [PMC]
  53. Thurzo A, Strunga M, Urban R, Surovková J, Afrashtehfar KI. Impact of Artificial Intelligence on Dental Education: A Review and Guide for Curriculum Update. Education Sciences. 2023;13(2):150. [Crossref]
  54. Li Y, Ye H, Ye F, et al. The Current Situation and Future Prospects of Simulators in Dental Education. J Med Internet Res. 2021;23(4):e23635. [Crossref]  [PubMed]  [PMC]
  55. Zatt FP, Rocha AO, Anjos LMD, Caldas RA, Cardoso M, Rabelo GD. Artificial intelligence applications in dentistry: A bibliometric review with an emphasis on computational research trends within the field. J Am Dent Assoc. 2024;155(9):755-764.e5. [Crossref]  [PubMed]
  56. Aminoshariae A, Kulild J, Nagendrababu V. Artificial Intelligence in Endodontics: Current Applications and Future Directions. J Endod. 2021;47(9):1352-1357. [Crossref]  [PubMed]
  57. Revilla-León M, Gómez-Polo M, Vyas S, Barmak BA, Galluci GO, Att W, et al. Artificial intelligence applications in implant dentistry: A systematic review. J Prosthet Dent. 2023;129(2):293-300. [Crossref]  [PubMed]
  58. Revilla-León M, Gómez-Polo M, Vyas S, et al. Artificial intelligence applications in restorative dentistry: A systematic review. J Prosthet Dent. 2022;128(5):867-875. [Crossref]  [PubMed]
  59. Revilla-León M, Gómez-Polo M, Barmak AB, et al. Artificial intelligence models for diagnosing gingivitis and periodontal disease: A systematic review. J Prosthet Dent. 2023;130(6):816-824. [Crossref]  [PubMed]
  60. Uzun Saylan BC, Baydar O, Yeşilova E, et al. Assessing the Effectiveness of Artificial Intelligence Models for Detecting Alveolar Bone Loss in Periodontal Disease: A Panoramic Radiograph Study. Diagnostics (Basel). 2023;13(10):1800. [Crossref]  [PubMed]  [PMC]
  61. Lee JH, Kim DH, Jeong SN, Choi SH. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci. 2018;48(2):114-123. [Crossref]  [PubMed]  [PMC]
  62. Karnik AP, Chhajer H, Venkatesh SB. Transforming Prosthodontics and oral implantology using robotics and artificial intelligence. Front Oral Health. 2024;5:1442100. [Crossref]  [PubMed]  [PMC]
  63. Heo MS, Kim JE, Hwang JJ, et al. Artificial intelligence in oral and maxillofacial radiology: what is currently possible?. Dentomaxillofac Radiol. 2021;50(3):20200375. [Crossref]  [PubMed]  [PMC]
  64. Yılmaz E, Kayikcioglu T, Kayipmaz S. Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography. Comput Methods Programs Biomed. 2017;146:91-100. [Crossref]  [PubMed]
  65. Tiwari A, Gupta N, Singla D, et al. Artificial Intelligence's Use in the Diagnosis of Mouth Ulcers: A Systematic Review. Cureus. 2023;15(9):e45187. [Crossref]
  66. Keser G, Bayrakdar İŞ, Pekiner FN, Çelik Ö, Orhan K. A deep learning algorithm for classification of oral lichen planus lesions from photographic images: A retrospective study. J Stomatol Oral Maxillofac Surg. 2023;124(1):101264. [Crossref]  [PubMed]
  67. Zhang W, Li J, Li ZB, Li Z. Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation. Sci Rep. 2018;8(1):12281. [Crossref]  [PubMed]  [PMC]
  68. Abdul NS, Shivakumar GC, Sangappa SB, et al. Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta-analysis. BMC Oral Health. 2024;24(1):122. [Crossref]  [PubMed]  [PMC]
  69. Rudolph DJ, Sinclair PM, Coggins JM. Automatic computerized radiographic identification of cephalometric landmarks. Am J Orthod Dentofacial Orthop. 1998;113(2):173-179. [Crossref]  [PubMed]
  70. Pham CV, Lee SJ, Kim SY, Lee S, Kim SH, Kim HS. Age estimation based on 3D post-mortem computed tomography images of mandible and femur using convolutional neural networks. PLoS One. 2021;16(5):e0251388. [Crossref]  [PubMed]  [PMC]
  71. Mahasantipiya PM, Yeesarapat U, Suriyadet T, Thaiupathump T. Bite mark identification using neural networks: A preliminary study, in: Proc. Int. MultiConference Engineers Computer Scientists, vol 1 Hong Kong, 2011. [Link]
  72. Vodanović M, Subašić M, Milošević DP, Galić I, Brkić H. Artificial intelligence in forensic medicine and forensic dentistry. J Forensic Odontostomatol. 2023;41(2):30-41. [PMC]