Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Monitoring oral health remotely: ethical considerations when using AI among vulnerable populations
12
Zitationen
3
Autoren
2025
Jahr
Abstract
Technological innovations in dentistry are revolutionizing the monitoring and management of oral health. This perspective article critically examines the rapid expansion of remote monitoring technologies-including artificial intelligence (AI)-driven diagnostics, electronic health records (EHR), wearable devices, mobile health applications, and chatbots-and discusses their ethical, legal, and social implications. The accelerated adoption of these digital tools, particularly in the wake of the COVID-19 pandemic, has enhanced accessibility to care while simultaneously raising significant concerns regarding patient consent, data privacy, and algorithmic biases. We review current applications ranging from AI-assisted detection of dental pathologies to blockchain-enabled data transfer within EHR systems, highlighting the potential for improved diagnostic accuracy and the risks associated with over-reliance on remote assessments. Furthermore, we underscore the challenges posed by the digital divide, where disparities in digital literacy and access may inadvertently exacerbate existing socio-economic and health inequalities. This article calls for the development and rigorous implementation of ethical frameworks and regulatory guidelines that ensure the reliability, transparency, and accountability of digital health innovations. By integrating multidisciplinary insights, our discussion aims to foster a balanced approach that maximizes the clinical benefits of emerging technologies while safeguarding patient autonomy and promoting equitable healthcare delivery.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.418 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.288 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.726 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.516 Zit.