[The challenges of artificial intelligence in odontology].
Les enjeux de l’intelligence artificielle en odontologie.
Journal
Medecine sciences : M/S
ISSN: 1958-5381
Titre abrégé: Med Sci (Paris)
Pays: France
ID NLM: 8710980
Informations de publication
Date de publication:
Jan 2024
Jan 2024
Historique:
medline:
1
2
2024
pubmed:
1
2
2024
entrez:
1
2
2024
Statut:
ppublish
Résumé
Artificial intelligence has numerous potential applications in dentistry, as these algorithms aim to improve the efficiency and safety of several clinical situations. While the first commercial solutions are being proposed, most of these algorithms have not been sufficiently validated for clinical use. This article describes the challenges surrounding the development of these new tools, to help clinicians to keep a critical eye on this technology. Les enjeux de l’intelligence artificielle en odontologie. Les applications potentielles de l’intelligence artificielle, ces algorithmes visant à améliorer l’efficacité et la sécurité de diverses décisions cliniques, sont nombreuses en odontologie. Alors que les premiers logiciels commerciaux commencent à être proposés, la plupart des algorithmes n’ont pas été solidement validés pour une utilisation clinique. Cet article décrit les enjeux entourant le développement de ces nouveaux outils, afin d’aider les praticiens à garder un regard éclairé et critique sur cette nouvelle approche.
Autres résumés
Type: Publisher
(fre)
Les enjeux de l’intelligence artificielle en odontologie.
Identifiants
pubmed: 38299907
doi: 10.1051/medsci/2023199
pii: msc230288
doi:
Types de publication
English Abstract
Journal Article
Langues
fre
Sous-ensembles de citation
IM
Pagination
79-84Informations de copyright
© 2024 médecine/sciences – Inserm.
Références
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015 ; 521 : 436–444.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019 ; 25 : 44–56.
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017 ; 542 : 115–118.
Keski-Filppula T, Nikki M, Haapea M, et al. Using artificial intelligence to detect chest X-rays with no significant findings in a primary health care setting in Oulu, Finland. 2022; https://doi.org/10.48550/arXiv.2205.08123.
Schwendicke F, Krois J. Data Dentistry: How Data Are Changing Clinical Care and Research. J Dent Res 2022; 101 : 21–9.
Mörch CM, Atsu S, Cai W, et al. Artificial Intelligence and Ethics in Dentistry: A Scoping Review. J Dent Res 2021; 100 : 1452–60.
Schwendicke F, Chaurasia A, Arsiwala L, et al. Deep learning for cephalometric landmark detection: systematic review and meta-analysis. Clin Oral Invest 2021; 25 : 4299–309.
Dot G, Schouman T, Dubois G, et al. Fully automatic segmentation of craniomaxillofacial CT scans for computer-assisted orthognathic surgery planning using the nnU-Net framework. Eur Radiol 2022; 32 : 3639–48.
Dot G, Schouman T, Chang S, et al. Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning. J Dent Res 2022; 101 : 1380–7.
Hao J, Liao W, Zhang YL, et al. Toward Clinically Applicable 3-Dimensional Tooth Segmentation via Deep Learning. J Dent Res 2022; 101 : 304–11.
Kühnisch J, Meyer O, Hesenius M, et al. Caries Detection on Intraoral Images Using Artificial Intelligence. J Dent Res 2022; 101 : 158–65.
Schwendicke F, Golla T, Dreher M, et al. Convolutional neural networks for dental image diagnostics: A scoping review. J Dent 2019 ; 91 : 103226.
Chen Q, Zhou X, Wu J, et al. Structuring electronic dental records through deep learning for a clinical decision support system. Health Informatics J 2021; 27 : 1460458220980036.
Rischke R, Schneider L, Müller K, et al. Federated Learning in Dentistry: Chances and Challenges. J Dent Res 2022; 101 : 1269–73.
Schwendicke F, Cejudo Grano de Oro J, Garcia Cantu A, et al. Artificial Intelligence for Caries Detection: Value of Data and Information. J Dent Res 2022; 101 : 1350–1356.
Schwendicke F, Singh T, Lee J-H, et al. Artificial intelligence in dental research: Checklist for authors, reviewers, readers. J Dent 2021; 107 : 103610.
Schwendicke F, Samek W, Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res 2020; 99 : 769–74.
Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis. Lancet Digital Health 2021; 3 : e195–e203.
Ma J, Schneider L, Lapuschkin S, et al. Towards Trustworthy AI in Dentistry. J Dent Res 2022; 101 : 1263–8.
Gaube S, Suresh H, Raue M, et al. Do as AI say: susceptibility in deployment of clinical decision-aids. npj Digit Med 2021; 4 : 31.
Ducret M, Mörch C, Richert R, et al. Intelligence artificielle en médecine orale : comprendre les enjeux éthiques et juridiques pour mieux s’y préparer. L’ Information dentaire 2022; 48–53.
Maddahi Y, Kalvandi M, Langman S, et al. RoboEthics in COVID-19: A Case Study in Dentistry. Front Robot AI 2021; 8 : 612740.
Mulligan S, Smith L, Martin N. Sustainable oral healthcare and the environment: challenges. Dent Update 2021; 48 : 493–501.
Martin N, Smith L, Mulligan S. Sustainable oral healthcare and the environment: mitigation strategies. Dent Update 2021; 48 : 524–31.
Hsu L-P, Huang Y-K, Chang Y-C. The implementation of artificial intelligence in dentistry could enhance environmental sustainability. J Dent Sci 2022; 17 : 1081–2.