Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Balancing Technological Advances with User Needs: User-centered Principles for AI-Driven Smart City Healthcare Monitoring
13
Zitationen
4
Autoren
2023
Jahr
Abstract
In recent years, the integration of artificial intelligence (AI) technologies has greatly benefited smart city healthcare, meeting the growing demand for affordable, efficient, and real-time healthcare services. Patient monitoring is one area where artificial intelligence has shown great promise. Improved health outcomes have been made possible by the advancement of AI-based monitoring systems, which enable more personalized and continuous patient monitoring. However, to fully maximize the benefits of these systems, a user-centered approach is essential, which prioritizes patients' needs and experiences while ensuring their privacy and autonomy are respected. This study focuses on the application of user-centered design principles in the development and deployment of AI-driven monitoring systems in smart city healthcare. Addressing the challenges and opportunities of AI-driven monitoring systems, the article considers issues such as privacy and security concerns, data accuracy, and user acceptance. Finally, some possible future directions to the challenges are suggested. A user-centered approach to AI monitoring systems is recommended for healthcare providers to enhance patient experience in smart city healthcare.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.214 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.071 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.429 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.418 Zit.