Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Governance, Lessons, and Future Trends for Scalable AI in Healthcare
0
Zitationen
4
Autoren
2025
Jahr
Abstract
This chapter addresses the critical ethical considerations, regulatory compliance requirements, and data privacy challenges inherent in scaling artificial intelligence (AI) for healthcare. Through industry-specific case studies, both successful implementations and notable setbacks are examined, providing valuable insights and best practices for responsible AI deployment. The chapter also delves into emerging technologies such as quantum computing that are poised to reshape AI in healthcare by enhancing computational power and data security. Additionally, future trends and strategic frameworks are discussed to support the development of adaptable, secure, and sustainable AI solutions tailored to the complex needs of healthcare systems. These combined perspectives offer a comprehensive roadmap for navigating the intricacies of AI scaling in a highly regulated environment.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.539 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.426 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.921 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 · 5.586 Zit.