Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Artificial intelligence and smile design: An e‐Delphi consensus statement of ethical challenges
18
Zitationen
7
Autoren
2024
Jahr
Abstract
PURPOSE: Smile design software increasingly relies on artificial intelligence (AI). However, using AI for smile design raises numerous technical and ethical concerns. This study aimed to evaluate these ethical issues. METHODS: An international consortium of experts specialized in AI, dentistry, and smile design was engaged to emulate and assess the ethical challenges raised by the use of AI for smile design. An e-Delphi protocol was used to seek the agreement of the ITU-WHO group on well-established ethical principles regarding the use of AI (wellness, respect for autonomy, privacy protection, solidarity, governance, equity, diversity, expertise/prudence, accountability/responsibility, sustainability, and transparency). Each principle included examples of ethical challenges that users might encounter when using AI for smile design. RESULTS: On the first round of the e-Delphi exercise, participants agreed that seven items should be considered in smile design (diversity, transparency, wellness, privacy protection, prudence, law and governance, and sustainable development), but the remaining four items (equity, accountability and responsibility, solidarity, and respect of autonomy) were rejected and had to be reformulated. After a second round, participants agreed to all items that should be considered while using AI for smile design. CONCLUSIONS: AI development and deployment for smile design should abide by the ethical principles of wellness, respect for autonomy, privacy protection, solidarity, governance, equity, diversity, expertise/prudence, accountability/responsibility, sustainability, and transparency.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.663 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.576 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.091 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.859 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Autoren
Institutionen
- Medical University Plovdiv(BG)
- King George's Medical University(IN)
- University of Puthisastra(KH)
- Université Claude Bernard Lyon 1(FR)
- Hospices Civils de Lyon(FR)
- Laboratoire de Mécanique des Contacts et des Structures(FR)
- Université Libre de Bruxelles(BE)
- Qatar University(QA)
- Centre National de la Recherche Scientifique(FR)
- Biologie Tissulaire et Ingénierie Thérapeutique(FR)