Opportunity or threat? A comparative study on the perception of artificial intelligence in dentistry among practitioners and patients
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Students’ Scientific Club, Department of Environmental Medicine and Epidemiology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland
Corresponding author
Miłosz Krysiak
Studenckie Koło Naukowe, Katedra i Zakład Medycyny i Epidemiologii Środowiskowej, Wydział Nauk Medycznych w Zabrzu ŚUM, ul. Jordana 19, 41-808 Zabrze
Ann. Acad. Med. Siles. 2025;1(nr specj.):51-58
KEYWORDS
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ABSTRACT
Introduction:
Artificial intelligence (AI) is increasingly being applied in dentistry – from radiological diagnostics and orthodontics to prosthesis design and practice management. AI’s precision, speed, and potential for automating routine tasks position it as a promising tool to support clinicians, although it simultaneously raises doubts regarding acceptance and trust among both dentists and patients.
Material and methods:
In May 2025, a cross-sectional online survey was conducted via Google Forms with 101 respondents: 25 dentists (24.75%) and 76 patients (75.25%). The questionnaire was comprised of both closed-ended and multiple-selection questions addressing AI awareness, trust levels in AI-supported diagnoses and treatment plans (rated on a 1–5 scale), therapeutic preferences, perceived benefits, and concerns regarding AI in dentistry.
Results:
The dental practitioners demonstrated higher trust in AI-supported diagnoses than patients, with mean trust scores of 2.92 versus 2.42, respectively. Trust levels increased significantly along with self-reported knowledge of AI, reaching mean values of 3.20 for “high” and 4.00 for “very high” familiarity. In a hypothetical scenario with equal error rates, 79.21% of respondents would favor a dentist supported by AI, 18.81% preferred human-only care, and only 1.98% would trust AI alone.
Conclusions:
AI in dentistry offers tangible diagnostic and organizational advantages; however, the principal barriers to its implementation are insufficient trust and limited user knowledge. One of the greatest challenges in modern public health is educating both patients and professionals about the capabilities and limitations of AI, a prerequisite for increasing acceptance and fully harnessing AI’s potential in clinical practice.
ACKNOWLEDGEMENTS
The authors wish to express their sincere gratitude to Dr. Joanna Zembala-John for her invaluable substantive supervision and helpful guidance throughout the research process.
FUNDING
This research received no external funding.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
REFERENCES (11)
1.
Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res. 2020;99(7):769–774. doi: 10.1177/0022034520915714.
2.
Ghaffari M., Zhu Y., Shrestha A. A review of advancements of artificial intelligence in dentistry. Dent Rev. 2024;4(2):100081. doi: 10.1016/j.dentre.2024.100081.
3.
Ding H, Wu J, Zhao W, Matinlinna JP, Burrow MF, Tsoi JKH. Artificial intelligence in dentistry – a review. Front Dent Med. 2023;4:1085251. doi: 10.3389/fdmed.2023.1085251.
4.
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. doi: 10.1016/j.adaj.2024.05.013.
5.
Awasthi R, Ramachandran SP, Mishra S, Mahapatra D, Arshad H, Atreja A, et al. Artificial intelligence in healthcare: 2024 year in review. medRxiv. 2025. doi: 10.1101/2025.02.26.25322978.
6.
Albano D, Galiano V, Basile M, Di Luca F, Gitto S, Messina C, et al. Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review. BMC Oral Health. 2024;24(1):274. doi: 10.1186/s12903-024-04046-7.
7.
Turosz N, Chęcińska K, Chęciński M, Brzozowska A, Nowak Z, Sikora M. Applications of artificial intelligence in the analysis of dental panoramic radiographs: an overview of systematic reviews. Dentomaxillofac Radiol. 2023;52(7):20230284. doi: 10.1259/dmfr.20230284.
8.
Olawade DB, Leena N, Egbon E, Rai J, Mohammed APEK, Oladapo BI, et al. AI-driven advancements in orthodontics for precision and patient outcomes. Dent J (Basel). 2025;13(5):198. doi: 10.3390/dj13050198.
9.
Revilla-León M, Gómez-Polo M, Vyas S, Barmak AB, Özcan M, Att W, et al. Artificial intelligence applications in restorative dentistry: A systematic review. J Prosthet Dent. 2022;128(5):867–875. doi: 10.1016/j.prosdent.2021.02.010.
10.
Fritsch SJ, Blankenheim A, Wahl A, Hetfeld P, Maassen O, Deffge S, et al. Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients. Digit Health. 2022;8:20552076221116772. doi: 10.1177/20552076221116772.
11.
Atkinson R, Flint J. Accessing hidden and hard-to-reach populations: snowball research strategies. Soc Res Update. 2001;33:1–4.