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Artificial intelligence in medico-dental diagnostics of the face: a narrative review of opportunities and challenges
OBJECTIVES: This review aims to share the current developments of artificial intelligence (AI) solutions in the field of medico-dental diagnostics of the face. The primary focus of this review is to present the applicability of artificial neural networks (ANN) to interpret medical images, together w...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708749/ https://www.ncbi.nlm.nih.gov/pubmed/36153437 http://dx.doi.org/10.1007/s00784-022-04724-2 |
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author | Patcas, Raphael Bornstein, Michael M. Schätzle, Marc A. Timofte, Radu |
author_facet | Patcas, Raphael Bornstein, Michael M. Schätzle, Marc A. Timofte, Radu |
author_sort | Patcas, Raphael |
collection | PubMed |
description | OBJECTIVES: This review aims to share the current developments of artificial intelligence (AI) solutions in the field of medico-dental diagnostics of the face. The primary focus of this review is to present the applicability of artificial neural networks (ANN) to interpret medical images, together with the associated opportunities, obstacles, and ethico-legal concerns. MATERIAL AND METHODS: Narrative literature review. RESULTS: Narrative literature review. CONCLUSION: Curated facial images are widely available and easily accessible and are as such particularly suitable big data for ANN training. New AI solutions have the potential to change contemporary dentistry by optimizing existing processes and enriching dental care with the introduction of new tools for assessment or treatment planning. The analyses of health-related big data may also contribute to revolutionize personalized medicine through the detection of previously unknown associations. In regard to facial images, advances in medico-dental AI-based diagnostics include software solutions for the detection and classification of pathologies, for rating attractiveness and for the prediction of age or gender. In order for an ANN to be suitable for medical diagnostics of the face, the arising challenges regarding computation and management of the software are discussed, with special emphasis on the use of non-medical big data for ANN training. The legal and ethical ramifications of feeding patients’ facial images to a neural network for diagnostic purposes are related to patient consent, data privacy, data security, liability, and intellectual property. Current ethico-legal regulation practices seem incapable of addressing all concerns and ensuring accountability. CLINICAL SIGNIFICANCE: While this review confirms the many benefits derived from AI solutions used for the diagnosis of medical images, it highlights the evident lack of regulatory oversight, the urgent need to establish licensing protocols, and the imperative to investigate the moral quality of new norms set with the implementation of AI applications in medico-dental diagnostics. |
format | Online Article Text |
id | pubmed-9708749 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-97087492022-12-01 Artificial intelligence in medico-dental diagnostics of the face: a narrative review of opportunities and challenges Patcas, Raphael Bornstein, Michael M. Schätzle, Marc A. Timofte, Radu Clin Oral Investig Review OBJECTIVES: This review aims to share the current developments of artificial intelligence (AI) solutions in the field of medico-dental diagnostics of the face. The primary focus of this review is to present the applicability of artificial neural networks (ANN) to interpret medical images, together with the associated opportunities, obstacles, and ethico-legal concerns. MATERIAL AND METHODS: Narrative literature review. RESULTS: Narrative literature review. CONCLUSION: Curated facial images are widely available and easily accessible and are as such particularly suitable big data for ANN training. New AI solutions have the potential to change contemporary dentistry by optimizing existing processes and enriching dental care with the introduction of new tools for assessment or treatment planning. The analyses of health-related big data may also contribute to revolutionize personalized medicine through the detection of previously unknown associations. In regard to facial images, advances in medico-dental AI-based diagnostics include software solutions for the detection and classification of pathologies, for rating attractiveness and for the prediction of age or gender. In order for an ANN to be suitable for medical diagnostics of the face, the arising challenges regarding computation and management of the software are discussed, with special emphasis on the use of non-medical big data for ANN training. The legal and ethical ramifications of feeding patients’ facial images to a neural network for diagnostic purposes are related to patient consent, data privacy, data security, liability, and intellectual property. Current ethico-legal regulation practices seem incapable of addressing all concerns and ensuring accountability. CLINICAL SIGNIFICANCE: While this review confirms the many benefits derived from AI solutions used for the diagnosis of medical images, it highlights the evident lack of regulatory oversight, the urgent need to establish licensing protocols, and the imperative to investigate the moral quality of new norms set with the implementation of AI applications in medico-dental diagnostics. Springer Berlin Heidelberg 2022-09-24 2022 /pmc/articles/PMC9708749/ /pubmed/36153437 http://dx.doi.org/10.1007/s00784-022-04724-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Review Patcas, Raphael Bornstein, Michael M. Schätzle, Marc A. Timofte, Radu Artificial intelligence in medico-dental diagnostics of the face: a narrative review of opportunities and challenges |
title | Artificial intelligence in medico-dental diagnostics of the face: a narrative review of opportunities and challenges |
title_full | Artificial intelligence in medico-dental diagnostics of the face: a narrative review of opportunities and challenges |
title_fullStr | Artificial intelligence in medico-dental diagnostics of the face: a narrative review of opportunities and challenges |
title_full_unstemmed | Artificial intelligence in medico-dental diagnostics of the face: a narrative review of opportunities and challenges |
title_short | Artificial intelligence in medico-dental diagnostics of the face: a narrative review of opportunities and challenges |
title_sort | artificial intelligence in medico-dental diagnostics of the face: a narrative review of opportunities and challenges |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708749/ https://www.ncbi.nlm.nih.gov/pubmed/36153437 http://dx.doi.org/10.1007/s00784-022-04724-2 |
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