Abstract
Maxime Ducret, Falk Schwendicke, Julien Cloarec
Abstract
Authors
Institutions
Provenance
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No local citing links have been materialized yet.
Data dentistry: how data are changing clinical care and research
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Consumer experiences with marketing technology
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Artificial intelligence for sustainable oral healthcare
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Improvement of mucosal lesion diagnosis with machine learning based on medical and semiological data: an observational study
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Publicly available dental image datasets for artificial intelligence
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Data sharing for responsible artificial intelligence in dentistry: a narrative review of legal frameworks and privacy-preserving techniques
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Federated vs local vs central deep learning of tooth segmentation on panoramic radiographs
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FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare
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Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility?
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Patient perceptions of artificial intelligence in dental imaging diagnostics: a multicentre survey
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Unresolved referenced work
Kept as external metadata until matched
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10.1038/s41591-025-03953-8 · doi-reference
Patient perceptions of artificial intelligence in dental imaging diagnostics: a multicentre survey
10.1093/dmfr/twaf018 · doi-reference
Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility?
10.3389/fsurg.2022.862322 · doi-reference
FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare
10.1136/bmj-2024-081554 · doi-reference
Federated vs local vs central deep learning of tooth segmentation on panoramic radiographs
10.1016/j.jdent.2023.104556 · doi-reference
Data sharing for responsible artificial intelligence in dentistry: a narrative review of legal frameworks and privacy-preserving techniques
10.1016/j.jdent.2025.106130 · doi-reference
Publicly available dental image datasets for artificial intelligence
10.1177/00220345241272052 · doi-reference
Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches
10.3389/frai.2024.1427517 · doi-reference
Machine learning-assisted prediction of clinical responses to periodontal treatment
10.1002/jper.24-0737 · doi-reference
Automatic recognition of teeth and periodontal bone loss measurement in digital radiographs using deep-learning artificial intelligence
10.1016/j.jds.2023.03.020 · doi-reference
Artificial intelligence for caries detection: value of data and information
10.1177/00220345221113756 · doi-reference
Improvement of mucosal lesion diagnosis with machine learning based on medical and semiological data: an observational study
10.3390/jcm11216596 · doi-reference
Empowering modern dentistry: the impact of artificial intelligence on patient care and clinical decision making
10.3390/diagnostics14121260 · doi-reference
Application of artificial intelligence in dentistry
10.1177/0022034520969115 · doi-reference
Communication tools and patient satisfaction: a scoping review
10.1111/jerd.12854 · doi-reference
Artificial intelligence models for diagnosing gingivitis and periodontal disease: a systematic review
10.1016/j.prosdent.2022.01.026 · doi-reference
Artificial intelligence for caries detection: randomized trial
10.1016/j.jdent.2021.103849 · doi-reference
10.1038/s41598-024-81724-0
10.1038/s41598-024-81724-0 · doi-reference
Foundation models for generalist medical artificial intelligence
10.1038/s41586-023-05881-4 · doi-reference
Tracking technologies in EHealth: revisiting the personalization-privacy paradox through the transparency-control framework
10.1016/j.techfore.2023.123101 · doi-reference
Too narrow to help? unveiling how recommendation agents’ specialization impacts user choices
10.1177/10949968251358181 · doi-reference
Artificial intelligence and management: the automation–Augmentation paradox
10.5465/amr.2018.0072 · doi-reference
Algorithm delegation: how embedded AI facilitates agency transference in medical services
10.1002/mar.70016 · doi-reference
Empowering GenAI stakeholders
10.1007/s11747-025-01098-1 · doi-reference
Algorithm aversion: people erroneously avoid algorithms after seeing them err
10.1037/xge0000033 · doi-reference
Overcoming consumer resistance to AI in general health care
10.1177/10949968221151061 · doi-reference
Consumers and artificial intelligence: an experiential perspective
10.1177/0022242920953847 · doi-reference
Artificial intelligence for sustainable oral healthcare
10.1016/j.jdent.2022.104344 · doi-reference
Resistance to medical artificial intelligence
10.1093/jcr/ucz013 · doi-reference
(Shane) from tools to agents: meta-analytic insights into human acceptance of AI
10.1177/00222429251355266 · doi-reference
Artificial intelligence and ethics in dentistry: a scoping review
10.1177/00220345211013808 · doi-reference
Towards trustworthy AI in dentistry
10.1177/00220345221106086 · doi-reference
From black box to glass box
10.1007/s11747-019-00710-5 · doi-reference
Deep learning for computer vision in dentistry: from black to glass box?
10.52768/2766-7820/3484 · doi-reference
Ethical considerations on artificial intelligence in dentistry: a framework and checklist
10.1016/j.jdent.2023.104593 · doi-reference
Trustworthy artificial intelligence in dentistry: learnings from the EU AI act
10.1177/00220345241271160 · doi-reference
The transformative role of artificial intelligence in dentistry: a comprehensive overview. Part 1: fundamentals of AI, and its contemporary applications in dentistry
10.1016/j.identj.2025.02.005 · doi-reference
Data dentistry: how data are changing clinical care and research
10.1177/00220345211020265 · doi-reference