Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Liability Standards for Medical Robotics and AI
3
Zitationen
1
Autoren
2022
Jahr
Abstract
Allocation of liability for harm caused at least partially by AI or medical robot can be based upon a binary distinction. The binary is the distinction between substitutive and complementary automation. When AI and robotics substitutes for a physician, strict liability is more appropriate than standard negligence doctrine. When the same technology merely assists a professional, a less stringent standard is appropriate. Such standards will help ensure that the deployment of advanced medical technologies is accomplished in a way that complements extant professionals’ skills, while promoting patient safety.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.560 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.451 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.