THE USE OF ARTIFICIAL INTELLIGENCE IN DENTAL PRACTICE AND PATIENTS’ ATTITUDES TOWARDS IT
Keywords:
Artificial intelligence, Dental practices, Digital transformation, Patient perceptionAbstract
Artificial intelligence is a broad field that encompasses various techniques and methods aimed at creating intelligent machines capable of performing tasks that typically require human intelligence. AI has definitely been rapidly expanding into the healthcare sector to improve medical care by analysing large amounts of data and providing insights that can aid in diagnosis and treatment decisions and incorporating digital intelligence into medical equipment and instruments can enhance procedures and improve patient outcomes. In conclusion, AI is rapidly advancing in the field of dentistry, offering numerous benefits, but it also brings ethical, privacy, and fairness considerations.
The study aims to identify the knowledge and attitude of the patients towards use of AI for diagnostics and treatment in dental practices.
Material and methods: The study involved 165 patients with an average age of 39.27 years, ranging from 18 to 60 years. A survey was conducted among patients who visited the clinic for a period of 2 months. The survey is anonymous, but the people who participated in it had to be over 18 years of age. The participants received a written instruction from the survey itself that it was necessary to indicate only one answer to each question.
Results: Out of the entire contingent of patients, 114 (69.10%) trust the doctor for an accurate diagnosis, and only 21 (12.72%) rely on an accurate diagnosis made by artificial intelligence (AI), almost 1/5 of the patients (30 people – 18.18%) cannot yet assess where their preferences and trust are leaning. For performing the treatment activities, themselves, more than half of the patients vote trust in the real medical activity - 106 people (64.24%) and only 24 people, which is 14.54%, would trust dental treatment performed by a machine controlled by a computer. Again, more than 1/5 of the respondents cannot decide (35 people – 21.22%), perhaps due to a lack of knowledge and idea about the type of treatment procedures managed by artificial intelligence.
Conclusion: The present study assessed the attitude of Bulgarian patients towards use of AI in diagnostics and treatment. The results show that in the traditionally manual profession as dentistry, patients rely in a large percentage (64.24%) on the dental work done by humans. The same applies to making a diagnosis by a doctor - 69.10% trust the doctor's competence and knowledge when making the diagnosis. Still a small percentage of Bulgarian patients believe in AI.
References
Adeoye, J., Koohi-Moghadam, M., Lo, A. W. I., Tsang, R. K. Y., Chow, V. L. Y., Zheng, L. W., Choi, S. W., Thomson, P., & Su, Y. X. (2021). Deep learning predicts the malignant-transformation-free survival of oral potentially malignant disorders. Cancers, 13(23). https://doi.org/10.3390/cancers13236054
Amjad, A., Kordel, P., & Fernandes, G. (2023). A Review on Innovation in Healthcare Sector (Telehealth) through Artificial Intelligence. Sustainability (Switzerland), 15(8), 1–24. https://doi.org/10.3390/su15086655
Avanzo, M., Trianni, A., Botta, F., Talamonti, C., Stasi, M., & Iori, M. (2021). Artificial intelligence and the medical physicist: Welcome to the machine. Applied Sciences (Switzerland), 11(4), 1–17. https://doi.org/10.3390/app11041691
Geis, J. R., Brady, A. P., Wu, C. C., Spencer, J., Ranschaert, E., Jaremko, J. L., Langer, S. G., Kitts, A. B., Birch, J., Shields, W. F., van den Hoven van Genderen, R., Kotter, E., Gichoya, J. W., Cook, T. S., Morgan, M. B., Tang, A., Safdar, N. M., & Kohli, M. (2019). Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement. Journal of the American College of Radiology, 16(11), 1516–1521. https://doi.org/10.1016/j.jacr.2019.07.028
Hegde, S., Ajila, V., Zhu, W., & Zeng, C. (2022). Artificial intelligence in early diagnosis and prevention of oral cancer. Asia-Pacific Journal of Oncology Nursing, 9(12), 100133. https://doi.org/10.1016/j.apjon.2022.100133
Hung, K. F., Yeung, A. W. K., Bornstein, M. M., & Schwendicke, F. (2023). Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging. Dentomaxillofacial Radiology, 52(1). https://doi.org/10.1259/dmfr.20220335
Huqh, M. Z. U., Abdullah, J. Y., Wong, L. S., Jamayet, N. Bin, Alam, M. K., Rashid, Q. F., Husein, A., Ahmad, W. M. A. W., Eusufzai, S. Z., Prasadh, S., Subramaniyan, V., Fuloria, N. K., Fuloria, S., Sekar, M., & Selvaraj, S. (2022). Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate—A Systematic Review. International Journal of Environmental Research and Public Health, 19(17). https://doi.org/10.3390/ijerph191710860
Kosan, E., Krois, J., Wingenfeld, K., Deuter, C. E., Gaudin, R., & Schwendicke, F. (2022). Patients’ Perspectives on Artificial Intelligence in Dentistry: A Controlled Study. Journal of Clinical Medicine, 11(8). https://doi.org/10.3390/jcm11082143
Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Müller, A., Mertens, S. M., Göstemeyer, G., Krois, J., & Schwendicke, F. (2021). Barriers and enablers for artificial intelligence in dental diagnostics: A qualitative study. Journal of Clinical Medicine, 10(8). https://doi.org/10.3390/jcm10081612
Nayyar, N., Ojcius, D. M., & Dugoni, A. A. (2020). The Role of Medicine and Technology in Shaping the Future of Oral Health. Journal of the California Dental Association, 48(3), 127–130. https://doi.org/10.1080/19424396.2020.12222558
Pacis, D. M. M., Subido, E. D. C., & Bugtai, N. T. (2018). Trends in telemedicine utilizing artificial intelligence. AIP Conference Proceedings, 1933. https://doi.org/10.1063/1.5023979
Reyes, L. T., Knorst, J. K., Ortiz, F. R., & Ardenghi, T. M. H. (2022). Machine Learning in the Diagnosis and Prognostic Prediction of Dental Caries: A Systematic Review. Caries Research, 56(3), 161–170. https://doi.org/10.1159/000524167
Schwendicke, F., & Krois, J. (2022). Data Dentistry: How Data Are Changing Clinical Care and Research. Journal of Dental Research, 101(1), 21–29. https://doi.org/10.1177/00220345211020265
Schwendicke, F., Rossi, J. G., Göstemeyer, G., Elhennawy, K., Cantu, A. G., Gaudin, R., Chaurasia, A., Gehrung, S., & Krois, J. (2021). Cost-effectiveness of Artificial Intelligence for Proximal Caries Detection. Journal of Dental Research, 100(4), 369–376. https://doi.org/10.1177/0022034520972335
Shetty, V., Yamamoto, J., & Yale, K. (2018). Re-architecting oral healthcare for the 21st century. Journal of Dentistry, 74(March), S10–S14. https://doi.org/10.1016/j.jdent.2018.04.017
Vodanović, M., Subašić, M., Milošević, D., & Pavičin, I. S. (2023). Artificial Intelligence in Medicine and Dentistry. Acta Stomatologica Croatica, 57(1), 70–84. https://doi.org/10.15644/asc57/1/8
Yordanova, G., & Gurgurova, G. (2021). Perception and feedback toward digital models and plaster casts in orthodontic patients. World Journal of Dentistry, 12(3), 173–177. https://doi.org/10.5005/jp-journals-10015-1824
Yordanova, G., Gurgurova, G., Kostov, I., & Georgieva, M. (n.d.). Software Orthodontics - Myth or Reality ? Technological Management of Clinical Practice. 2023 International Scientific Conference on Computer Science (COMSCI), 1–4. https://doi.org/10.1109/COMSCI59259.2023.10315886
Йорданова, Г., М. Г. (2021). Комуникации между дентален лекар и пациент - иновативни практики в дейността. Научни Трудове МВБУ, 13, 52–92. issn: 1313-0846