Application of machine learning in dentistry: insights, prospects and challenges
Background: Machine learning (ML) is transforming dentistry by setting new standards for precision and efficiency in clinical practice, while driving improvements in care delivery and quality. Objectives: This review: (1) states the necessity to develop ML in dentistry for the purpose of breaking...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Medical Journals Sweden
2025-03-01
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| Series: | Acta Odontologica Scandinavica |
| Subjects: | |
| Online Access: | https://medicaljournalssweden.se/actaodontologica/article/view/43345 |
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| Summary: | Background: Machine learning (ML) is transforming dentistry by setting new standards for precision and efficiency in clinical practice, while driving improvements in care delivery and quality.
Objectives: This review: (1) states the necessity to develop ML in dentistry for the purpose of breaking the limitations of traditional dental technologies; (2) discusses the principles of ML-based models utilised in dental clinical practice and care; (3) outlines the application respects of ML in dentistry; and (4) highlights the prospects and challenges to be addressed.
Data and sources: In this narrative review, a comprehensive search was conducted in PubMed/MEDLINE, Web of Science, ScienceDirect, and Institute of Electrical and Electronics Engineers (IEEE) Xplore databases.
Conclusions: Machine Learning has demonstrated significant potential in dentistry with its intelligently assistive function, promoting diagnostic efficiency, personalised treatment plans and related streamline workflows. However, challenges related to data privacy, security, interpretability, and ethical considerations were highly urgent to be addressed in the next review, with the objective of creating a backdrop for future research in this rapidly expanding arena.
Clinical significance: Development of ML brought transformative impact in the fields of dentistry, from diagnostic, personalised treatment plan to dental care workflows. Particularly, integrating ML-based models with diagnostic tools will significantly enhance the diagnostic efficiency and precision in dental surgeries and treatments.
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| ISSN: | 0001-6357 1502-3850 |