Applications of Artificial Intelligence in Dental Medicine: A Critical Review
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INTRODUCTION: Artificial intelligence (AI), including its subfields of machine learning and deep learning, is a branch of computer science and engineering focused on creating machines capable of tasks requiring human-like intelligence, such as visual perception, decision-making, and natural language processing. AI applications have become increasingly prevalent in dental medicine, generating high expectations as well as raising ethical and practical concerns. METHODS: This critical review evaluates the current applications of AI in dentistry, identifying key perspectives, challenges, and limitations in ongoing AI research. RESULTS: AI models have been applied across various dental specialties, supporting diagnosis, treatment planning, and decision-making, while also reducing the burden of repetitive tasks and optimizing clinical workflows. However, ethical complexities and methodological limitations, such as inconsistent data quality, bias risk, lack of transparency, and limited clinical validation, undermine the quality of AI studies and hinder the effective integration of AI into routine dental practice. CONCLUSIONS: To improve AI research, studies must adhere to standardized methodological and ethical guidelines, particularly in data collection, while ensuring transparency, privacy, and accountability. Developing a comprehensive framework for producing robust, reproducible AI research and clinically validated technologies will facilitate the seamless integration of AI into clinical practice, benefiting both clinicians and patients by improving dental care.
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Artificial intelligence has emerged as a promising clinical decision-support tool in dentistry.
“In this context, artificial intelligence (AI) has emerged as a promising clinical decision-support tool in dentistry [26][27][28].”
From Comparing Artificial Intelligence (ChatGPT, Gemini, DeepSeek) and Oral Surgeons in Detecting Clinically Relevant Drug–Drug Interactions in Dental Therapy (2025) · cites this paper as [28]
The inclusion of five distinct LLM architectures provides a comprehensive view rarely explored in dental informatics.
“The inclusion of five distinct LLM architectures (general-purpose and pharmacologically oriented) also provides a comprehensive view rarely explored in dental informatics, complementing the multidomain analyses discussed by Schwendicke et al. (2020) [27] and Sitaras et al. (2025) [28].”
From Comparing Artificial Intelligence (ChatGPT, Gemini, DeepSeek) and Oral Surgeons in Detecting Clinically Relevant Drug–Drug Interactions in Dental Therapy (2025) · cites this paper as Sitaras et al. (2025) [28]
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Flagged first, then by citations
Research works printed but not linked: 4
- [50]IEEE Engineering in Medicine and Biology Society 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): biomedical engineering ranging from wellness to intensive care Berlin 2019
- [77]Sjogren U Hagglund B Sundqvist G Wing K Factors affecting the long-term results of endodontic treatment J Endod 16 10 2019 498 504 2084204
- [113]Jiang F Jiang Y Zhi H Dong Y Li H Ma S Artificial intelligence in healthcare: past, present and future Stroke Vasc Neurol 2 2017 230 243 29507784 PMC5829945
- [116]Schwendicke F Golla T Dreher M Krois J Convolutional neural networks for dental image diagnostics: a scoping review J Dent 91 2019 103226 31704386
Cited by
Sorted by citations
Tayeb, S., Barausse, C., Pellegrino, G., Sansavini, M., Pistilli, R., & Felice, P. (2025) · Applied Sciences · doi:10.3390/app152312851 · Cited by 12 · see citing works
- Authors
- Sitaras, S., Tsolakis, I. A., Gelsini, M., Tsolakis, A. I., Schwendicke, F., Wolf, T. G., & Perlea, P.
- Affiliations
- Aristotle University of Thessaloniki (GR)
- National and Kapodistrian University of Athens (GR)
- Ludwig-Maximilians-Universität München (DE)
- University of Bern (CH)
- Carol Davila University of Medicine and Pharmacy (RO)
- Corresponding author
- Wolf, Thomas Gerhard
- Venue
- International Dental Journal
- Publisher
- Elsevier BV
- Published
- 2025-04
- Volume
- 75
- Issue
- 2
- Pages
- 474-486
- ISSN
- 0020-6539 (ISSN-L)
- PMC
- PMC11976566
- OpenAlex
- W4406675292
- Cited by
- 42
- References
- 131
- Citations by year
- 2026: 29 · 2025: 13
- FWCI
- 9.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Dental Journal (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Open Access CRIS of the University of Bern
- Funders
- University of Bern
- Topics
- Health Sciences › Dentistry › Oral Surgery › Dental Radiography and Imaging
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- artificial intelligence in dentistry, machine learning, deep learning, dental diagnosis, treatment planning, clinical decision support, clinical workflow optimization, AI ethics, algorithmic bias, data quality, clinical validation, reproducibility, privacy
- MeSH terms
- Deep Learning, Artificial Intelligence, Dentistry, Humans
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