The Role of Artificial Intelligence in Dental Diagnosis

Sabira Arefin

doi:10.18535/ijsrm/v12i07.mp05

Abstract

This paper discusses the potential role of artificial intelligence in enhancing dental diagnosis and care delivery. It describes how AI shows promise in automatic detection of abnormalities in dental images, personalised disease risk assessment through predictive modelling, and generation of comprehensive examination reports. The ability of AI to analyse patterns across medical imaging, genetics, lifestyle factors and health records enables more holistic understanding of oral health in relation to systemic conditions. Clinical decision support through differential diagnoses and evidence-based treatment recommendations aims to augment dentists' decision making. Seamless integration of AI into clinical workflows through interfaces is emphasised as important for adoption. Challenges around data and model validation are also addressed. Continued development of AI aims to realise benefits like earlier disease identification and more proactive, personalised care approaches

References

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[refR-1] Series Science. (2023, December 27). Artificial Intelligence in Healthcare: Explore the Applications of AI in Various Medical Domains, Such as Medical Imaging, Diagnosis, Drug Discovery, and Patient Care. SeriesScience International | Open Access Journals | Peer Reviewed Articles. https://seriesscience.com/artificial-intelligence-healthcare/#:~:text=Ensuring%20the%20robustness%20and%20generalizabilityGoogle Scholar ↗

[refR-2] Alotaibi, G., Awawdeh, M., Farook, F. F., Aljohani, M., Aldhafiri, R. M., & Aldhoayan, M. (2022). Artificial intelligence (AI) diagnostic tools: utilizing a convolutional neural network (CNN) to assess periodontal bone level radiographically—a retrospective study. BMC Oral Health, 22(1). https://doi.org/10.1186/s12903-022-02436-3DOI ↗Google Scholar ↗

[refR-3] Batra, A. M., Reche, A., Batra, A. M., & Reche, A. (2023). A New Era of Dental Care: Harnessing Artificial Intelligence for Better Diagnosis and Treatment. Cureus, 15(11). https://doi.org/10.7759/cureus.49319DOI ↗Google Scholar ↗

[refR-4] Bethesda. (2021). Effect of Oral Health on the Community, Overall Well-Being, and the Economy. In www.ncbi.nlm.nih.gov. National Institute of Dental and Craniofacial Research (US). https://www.ncbi.nlm.nih.gov/books/NBK578297/Google Scholar ↗

[refR-5] Engle, R. L., Mohr, D. C., Holmes, S. K., Seibert, M. N., Afable, M., Leyson, J., & Meterko, M. (2021). Evidence-based practice and patient-centered care: Doing both well. Health Care Management Review, 46(3), 174–184. https://doi.org/10.1097/HMR.0000000000000254DOI ↗Google Scholar ↗

[refR-6] Florkow, M. C., Willemsen, K., Mascarenhas, V. V., Oei, E. H. G., Stralen, M., & Seevinck, P. R. (2022). Magnetic Resonance Imaging Versus Computed Tomography for Three‐Dimensional Bone Imaging of Musculoskeletal Pathologies: A Review. Journal of Magnetic Resonance Imaging. https://doi.org/10.1002/jmri.28067DOI ↗Google Scholar ↗

[refR-7] Haleem, A., Javaid, M., Singh, R. P., & Suman, R. (2021). Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sensors International, 2(2). https://doi.org/10.1016/j.sintl.2021.100117DOI ↗Google Scholar ↗

[refR-8] Krishnan, G., Singh, S., Pathania, M., Siddharth Gosavi, Shuchi Abhishek, Ashwin Parchani, & Dhar, M. (2023). Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1227091DOI ↗Google Scholar ↗

[refR-9] Medigy. (2024, January 16). Artificial Intelligence. Medigy.com. https://www.medigy.com/topic/healthcare-it-news-artificial-intelligence/Google Scholar ↗

[refR-10] Ng, K., Sun, J., Hu, J., & Wang, F. (2015). Personalized Predictive Modeling and Risk Factor Identification using Patient Similarity. AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science, 2015, 132–136. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4525240/Google Scholar ↗

[refR-11] Rauschecker, A. M., Rudie, J. D., Xie, L., Wang, J., Duong, M. T., Botzolakis, E. J., Kovalovich, A. M., Egan, J., Cook, T. C., Bryan, R. N., Nasrallah, I. M., Mohan, S., & Gee, J. C. (2020). Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI. Radiology, 295(3), 626–637. https://doi.org/10.1148/radiol.2020190283DOI ↗Google Scholar ↗

[refR-12] Scheinfeld, M. H., Shifteh, K., Avery, L. L., Dym, H., & Dym, R. J. (2012). Teeth: What Radiologists Should Know. RadioGraphics, 32(7), 1927–1944. https://doi.org/10.1148/rg.327125717DOI ↗Google Scholar ↗

[refR-13] Tözüm, T. F., & Taguchi, A. (2004). Role of dental panoramic radiographs in assessment of future dental conditions in patients with osteoporosis and periodontitis. The New York State Dental Journal, 70(1), 32–35. https://pubmed.ncbi.nlm.nih.gov/15042795/Google Scholar ↗

[refR-14] Vodanović, M., Marko Subašić, Milošević, D., & Ivana Savić Pavičin. (2023). Artificial Intelligence in Medicine and Dentistry. Acta Stomatologica Croatica, 57(1), 70–84. https://doi.org/10.15644/asc57/1/8DOI ↗Google Scholar ↗

[refR-15] Arefin, S. (2024). Health Equity and Solutions in the United States Healthcare System. Integrated Journal of Science and Technology, 1(5), 1-9.Google Scholar ↗

[refR-16] Arefin, S., Kipkoech, G., & Arefin, S. (2024). Nurturing Mental Strength and Fostering Inclusive Leadership in Women. Innovative Social Sciences Journal, 10(1), 1-8.Google Scholar ↗