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- MakaleAçık Erişim2025Scopus SJRQ1Towards precision dentistry through artificial intelligence and blockchain-based digital Twins: Investigating challenges and solution strategies
Erol, Ismail; Peker, Iskender; Medeni, Ihsan Tolga; Yuce, Fatma
The integration of artificial intelligence (AI), blockchain, and digital twins (DT) in dentistry is essential to meet evolving patient expectations and global healthcare challenges. However, realizing their full potential requires addressing significant adoption barriers. While conceptual studies have explored challenges to implementing AI, blockchain, and DT in dentistry, no prior research has systematically analyzed the interrelationships among these obstacles using a structured decision framework. This study employs pentagonal fuzzy (PF) DEMATEL to investigate the connections among challenges to effectively implementing AI and blockchain-based DT in dentistry. Expert opinions highlight ethical considerations and bias and fairness in AI-based DT as the most critical causal challenges. We propose an AI and blockchain-based DT architecture that enables human doctors to leverage DT-based decision support, while AI-driven systems execute high-precision procedures with blockchain-verified workflows. This framework enhances treatment accuracy and personalization in dentistry and offers a scalable model for addressing societal concerns, such as privacy, fairness, and labor dynamics, in technology-driven ecosystems. A pilot study framework is proposed to validate diagnostic accuracy, data security, and treatment customization. Accompanied by stakeholder guidelines, this research bridges dentistry with broader discussions on technology's societal impact, emphasizing the need for interdisciplinary strategies to ensure equitable and trustworthy innovation.
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İncele - MakaleYalnız künye2019Scopus SJRQ2Exploring the value of using data: a case study of continuous improvement through data warehousing
Rogers, W. Pratt; Kahraman, M. Mustafa; Dessureault, Sean
The mining industry is adapting data-rich technology. Data management processes and infrastructure require additional investment which, can be difficult to justify. A valuation model for data management infrastructure would necessitate modelling the ‘use’ of data and quantifying the value and the likelihood of success of improvement initiatives. A model is presented which can assess the impacts of a data-driven initiative. The initiatives are shown to be the foundation of improved shift coordination resulting in significant and sustained operational improvements. This paper aims to illustrate the value of using data from source systems in mining operations.
AtıfScopus9WoS8OpenAlex11İncele