Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
AI-driven Health Data Governance: The Risks and Challenges of Datafication
0
Zitationen
3
Autoren
2025
Jahr
Abstract
Artificial intelligence (AI) integration in healthcare is transforming data governance and profoundly impacting medical practice.While AI promises advancements in diagnostic accuracy and personalized treatments, it also raises major concerns regarding data security, data privacy, fairness, and the autonomy of healthcare professionals.This article examines how the datafication of healthcare, where medical data becomes a valuable and contested resource, generates new ethical issues regarding the access to medical data, patient consent, and data security.Based on a thorough literature review, the article highlights key challenges in the AI-driven medical data governance and identifies potential risks of datafication, such as fragmentation of access, security breaches, patient misinformation, unauthorized medical data use, data commercialization, or the erosion of medical privacy.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.561 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.452 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.948 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.797 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.