Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Transforming Wellness
0
Zitationen
1
Autoren
2025
Jahr
Abstract
In order to enhance precision wellness with AI and machine learning (ML), it is imperative to solve ethical and data protection problems. In order to provide individualized healthcare solutions, AI-driven systems employ sensitive health data, such as genetic information and biometric readings. This raises questions around patient consent, data security, algorithmic fairness, and healthcare injustice. One major concern is algorithmic bias, since AI models have the potential to reinforce inequality. Fairness must be ensured by developers using a variety of datasets and frequent monitoring. Another issue is getting patients to give their informed consent, since sophisticated AI algorithms make it hard for them to completely comprehend how their data is used. To develop trust, one must be able to explain things. Ensuring data privacy necessitates stringent security protocols and adherence to laws like as GDPR and HIPAA. Enhancing patient understanding and engagement through the integration of cognitive science concepts can lead to better AI system design.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.534 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.423 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.917 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 · 5.582 Zit.