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VOLUME 15 , ISSUE 2 ( July-December, 2023 ) > List of Articles

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Advanced Dentistry: Transforming Patient Care with Artificial Intelligence

Ayush Ahluwalia, Ayushi Gautam, Sahil S Thakar

Keywords : Artificial intelligence, Black box, Dentistry, Heuristics, Partial least squares and artificial neural network, Patient care

Citation Information : Ahluwalia A, Gautam A, Thakar SS. Advanced Dentistry: Transforming Patient Care with Artificial Intelligence. CODS J Dent 2023; 15 (2):64-69.

DOI: 10.5005/jp-journals-10063-0149

License: CC BY-NC 4.0

Published Online: 17-05-2024

Copyright Statement:  Copyright © 2023; The Author(s).


Abstract

Artificial intelligence (AI) has already revolutionized the fundamental operations of various sectors today, yet its integration into the healthcare sector remains in its infancy. This integration holds tremendous potential to bring about transformative effects, particularly in the advancement of patient-centered care. In a critical sector like health care, where even the smallest decision can shape treatment outcomes significantly, the integration of AI reveals its complexity. While on the one hand, AI holds promise in aiding doctors to reach accurate diagnoses faster, on the contrary, it presents its own set of challenges. This review aims to outline the current issues in the healthcare sector, explore the role of AI in addressing these challenges, summarize recent advances in AI within dentistry, and examine the key challenges in this integration.


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  1. Anyoha R. The History of Artificial Intelligence. Science in the News. Updated August 28, 2017. Accessed February 22, 2024. https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/
  2. Ding H, Wu J, Zhao W, et al. Artificial intelligence in dentistry—a review. Front Dent Med 2023;4:1085251. DOI: 10.3389/fdmed.2023.1085251
  3. Davidson CS. The caring physician: the life of Dr. Francis W. Peabody. N Engl J Med 1993;328:817–818. DOI: 10.1056/NEJM199303183281123
  4. Mercer SW, Hasegawa H, Reilly D, et al. Length of consultations. Time and stress are limiting holistic care in Scotland. BMJ 2002;325(7374):1241. PMID: 12455103.
  5. Irving G, Neves AL, Dambha-Miller H, et al. International variations in primary care physician consultation time: a systematic review of 67 countries. BMJ Open 2017;7(10):e017902. DOI: 10.1136/bmjopen-2017-017902
  6. Singletary B, Patel N, Heslin M. Patient perceptions about their physician in 2 words: The good, the bad, and the ugly. JAMA Surg 2017;152(12):1169–1170. DOI: 10.1001/jamasurg.2017.3851
  7. Kulkarni S, Dagli N, Duraiswamy P, et al. Stress and professional burnout among newly graduated dentists. J Int Soc Prev Community Dent 2016;6(6):535–541. DOI: 10.4103/2231-0762.195509
  8. Kane L. ‘I cry but no one cares’: Physician burnout & depression report 2023. Medscape. Updated January 20, 2023. Accessed February 22, 2024. https://www.staging.medscape.com/slideshow/2023-lifestyle-burnout-6016058?reg=1#29
  9. Moreno T, Sanz JL, Melo M, et al. Overtreatment in restorative dentistry: Decision making by last-year dental students. Int J Environ Res Public Health 2021;18(23):12585. DOI: 10.3390/ijerph182312585
  10. Hans MK, Hans R, Nagpal A. Quackery: a major loophole in dental practice in India. J Clin Diagn Res 2014;8(2):283. DOI: 10.7860/JCDR/2014/6820.4081
  11. von Eckardstein KL, Keil M, Rohde V. Unnecessary dental procedures as a consequence of trigeminal neuralgia. Neurosurg Rev 2015;38(2):355–360. DOI: 10.1007/s10143-014-0591-1
  12. Marathe S, Hunter BM, Chakravarthi I, et al. The Impacts of corporatisation of healthcare on medical practice and professionals in Maharashtra, India. BMJ Global Health 2020;5(2):e002026. DOI: 10.1136/bmjgh-2019-002026
  13. Mohan M, Ravindran TKS. Unemployment and vulnerable financial situation among recent dental graduates of Kerala, India - results from a cross-sectional study. J Global Oral Health 2019;1(1):49–57. DOI: 10.25259/JGOH-12-2018
  14. Ramanarayanan V, Janakiram C, Joseph J, et al. Oral health care system analysis: a case study from India. J Family Med Prim Care 2020;9(4):1950–1957. DOI: 10.4103/jfmpc.jfmpc_1191_19
  15. Holden A. Do I really need this crown? Dentists admit feeling pressured to offer unnecessary treatments. The Conversation. Updated November 2, 2020. Accessed February 22, 2024. https://theconversation.com/do-i-really-need-this-crown-dentists-admit-feeling-pressured-to-offer-unnecessary-treatments-148638
  16. Djulbegovic B, Hozo I, Beckstead J, et al. Dual processing model of medical decision-making. BMC Med Inform Decis Mak 2012;12:94. DOI: 10.1186/1472-6947-12-94
  17. Whelehan DF, Conlon KC, Ridgway PF. Medicine and heuristics: cognitive biases and medical decision-making. Ir J Med Sci 2020;189(4):1477–1484. DOI: 10.1007/s11845-020-02235-1
  18. Kahneman D, Tversky A. Prospect theory: an analysis of decision under risk. Econometrica 1979;47(2):263–291. DOI: 10.2307/1914185
  19. Hussain A, Oestreicher J. Clinical decision-making: heuristics and cognitive biases for the ophthalmologist. Surv Ophthalmol 2017;63(1):119–124. DOI: 10.1016/j.survophthal.2017.08.007
  20. Rehder O, Noack MJ, Zirkel C, et al. Recognition and prevention of cognitive biases and judgment errors in diagnostics and dental therapy. Dtsch Zahnärztl Z Int 2021;3:231–237. DOI: 10.3238/dzz-int.2021.0028
  21. Nagy M, Sisk B. How will artificial intelligence affect patient-clinician relationships? AMA J Ethics 2020;22(5):E395–E400. DOI: 10.1001/amajethics.2020.395
  22. Rowley R. AI as a way to overcome cognitive bias in physicians. CIO. Updated June 27, Accessed February 22, 2024. https://www.cio.com/article/230275/ai-as-a-way-to-overcome-cognitive-bias-in-physicians.html
  23. Tsai A. How chatGPT4 save a dog's life. Medium. Updated March 29, 2023. Accessed February 22, 2024. https://medium.com/@albertfetsai/how-chatgpt4-save-a-dogs-life-a8c67561f01f
  24. Gordon C. AI innovations in healthcare. Forbes. Updated September 30, 2021. Accessed February 22, 2024. https://www.forbes.com/sites/cindygordon/2021/09/30/ai-innovations-in-healthcare/?sh=47c89b3d36ed
  25. How artificial intelligence is accelerating innovation in healthcare. Goldman Sachs. Research. Updated April 26, 2023. Accessed February 22, 2024. https://www.goldmansachs.com/intelligence/pages/how-artificial-intelligence-is-accelerating-innovation-in-healthcare.html
  26. Orthodontic Solutions. ORCA Dental AI. c2024. Accessed February 22, 2024. https://www.orca-ai.com/solutions/orthodontics/
  27. Takeda S, Mine Y, Yoshimi Y, et al. Landmark annotation and mandibular lateral deviation analysis of posteroanterior cephalograms using a convolutional neural network. J Dent Sci 2021;16(3):957–963. DOI: 10.1016/j.jds.2020.10.012
  28. Patcas R, Bernini DAJ, Volokitin A, et al. Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. Int J Oral Maxillofac Surg 2019;48(1):77–83. DOI: 10.1016/j.ijom.2018.07.010
  29. Lerner H, Mouhyi J, Admakin O, et al. Artificial intelligence in fixed implant prosthodontics: a retrospective study of 106 implant-supported monolithic zirconia crowns inserted in the posterior jaws of 90 patients. BMC Oral Health 2020;20(1):80. DOI: 10.1186/s12903-020-1062-4
  30. Rana A, Yauney G, Wong LC, et al. Automated segmentation of gingival diseases from oral images. IEEE Healthcare Innov Point Care Technol (HI-POCT) 2017:144–147. DOI: 10.1109/HIC.2017.8227605
  31. Takahashi T, Nozaki K, Gonda T, et al. Identification of dental implants using deep learning-pilot study. Int J Implant Dent 2020;6(1):53. DOI: 10.1186/s40729-020-00250-6
  32. Cha JY, Yoon HI, Yeo IS, et al. Peri-implant bone loss measurement using a region-based convolutional neural network on dental periapical radiographs. J Clin Med 2021;10(5):1009. DOI: 10.3390/jcm10051009
  33. Hiraiwa T, Ariji Y, Fukuda M, et al. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofac Radiol 2019;48(3):20180218. doi:10.1259/dmfr.20180218
  34. Lahoud P, EzEldeen M, Beznik T, et al. Artificial intelligence for fast and accurate 3-dimensional tooth segmentation on cone-beam computed tomography. J Endod 2021;47(5):827–835. DOI: 10.1016/j.joen.2020.12.020
  35. Study root canal morphology and anatomy with Diagnocat. Diagnocat. Updated March 19, 2024. Accessed February 22, 2024. https://diagnocat.com/camx/case-studies/study-root-canal-morphology-and-anatomy-with-diagnocat/
  36. Chugal NM, Clive JM, Spångberg LS. Endodontic infection: some biologic and treatment factors associated with outcome. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2003;96(1):81–90. DOI: 10.1016/s1079-2104(02)91703-8
  37. Saghiri MA, Garcia-Godoy F, Gutmann JL, et al. The reliability of artificial neural network in locating minor apical foramen: a cadaver study. J Endod 2012;38(8):1130–1134. DOI: 10.1016/j.joen.2012.05.004
  38. Stehrer R, Hingsammer L, Staudigl C, et al. Machine learning based prediction of perioperative blood loss in orthognathic surgery. J Craniomaxillofac Surg 2019;47(11):1676–1681. doi: 10.1016/j.jcms.2019.08.005
  39. Vranckx M, Van Gerven A, Willems H, et al. Artificial intelligence (AI)-driven molar angulation measurements to predict third molar eruption on panoramic radiographs. Int J Environ Res Public Health 2020;17(10):3716. DOI: 10.3390/ijerph17103716
  40. Yoo JH, Yeom HG, Shin W, et al. Deep learning based prediction of extraction difficulty for mandibular third molars. Sci Rep 2021;11(1):1954. DOI: 10.1038/s41598-021-81449-4
  41. Ahn Y, Hwang JJ, Jung YH, et al. Automated mesiodens classification system using deep learning on panoramic radiographs of children. Diagnostics 2021;11(8):1477. DOI: 10.3390/diagnostics11081477
  42. Fu Q, Chen Y, Li Z, et al. A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: a retrospective study. E Clin Med 2020;27:100558. DOI: 10.1016/j.eclinm.2020.100558
  43. Wang CY, Tsai T, Chen HM, et al. PLS-ANN based classification model for oral submucous fibrosis and oral carcinogenesis. Lasers Surg Med 2003;32(4):318–326. DOI: 10.1002/lsm.10153
  44. Patel S, Dawood A, Mannocci F, et al. Detection of periapical bone defects in human jaws using cone-beam computed tomography and intraoral radiography. Int Endod J 2009;42(6):507–515. DOI: 10.1111/j.1365-2591.2008.01538.x
  45. Aminoshariae A, Kulild J, Nagendrababu V, et al. Artificial intelligence in endodontics: current applications and future directions. J Endod 2021;47(9):1352–1357. DOI: 10.1016/j.joen.2021.06.003
  46. Setzer FC, Shi KJ, Zhang Z, et al. Artificial intelligence for the computer-aided detection of periapical lesions in cone-beam computed tomographic images. J Endod 2020;46(7):987–993. DOI: 10.1016/j.joen.2020.03.025
  47. Abdolali F, Zoroofi RA, Otake Y, et al. Automatic segmentation of maxillofacial cysts in cone-beam CT images. Comput Biol Med 2016;72:108–119. DOI: 10.1016/j.compbiomed.2016.03.014
  48. Ekert T, Krois J, Meinhold L, et al. Deep learning for the radiographic detection of apical lesions. J Endod 2019;45(7):917–922.e5. DOI: 10.1016/j.joen.2019.03.016
  49. Lee JH, Kim DH, Jeong SN, et al. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent 2018;77:106–111. DOI: 10.1016/j.jdent.2018.07.015
  50. Topol E. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. New York: Basic Books; 2019.
  51. Barr A. Google mistakenly tags black people as ‘gorillas,’ showing limits of algorithms. Wall Street J. Updated July 1, 2015. Accessed February 22, 2024. https://www.wsj.com/articles/BL-DGB-42522
  52. Ma MA, Gutiérrez DE, Frausto JM, et al. Minority representation in clinical trials in the United States: trends over the past 25 years. Mayo Clin Proc 2021;96(1):264–266. DOI: 10.1016/j.mayocp.2020.10.027
  53. Brouillette M. Deep learning is a black box, but health care won't mind. MIT Technol Rev. Updated April 27, 2017. Accessed February 22, 2024. https://www.technologyreview.com/2017/04/27/242905/deep-learning-is-a-black-box-but-health-care-wont-mind/
  54. Lohn A. Hacking poses risks for artificial intelligence. Center Sec Emerg Technol. Updated March 1, 2022. Accessed February 22, 2024. https://cset.georgetown.edu/article/hacking-poses-risks-for-artificial-intelligence/
  55. Parikh RB, Obermeyer Z, Navathe AS. Regulation of predictive analytics in medicine. Science 2019;363(6429):810–812. DOI: 10.1126/science.aaw0029
  56. DeCamp M, Tilburt JC. Why we cannot trust artificial intelligence in medicine. Lancet Digit Health 2019;1(8):e390. DOI: 10.1016/S2589-7500(19)30197-9
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