Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Challenges of Artificial Intelligence in Medicine
0
Zitationen
12
Autoren
2023
Jahr
Abstract
Medical technologies bolstered by AI are quickly developing into viable options for actual clinical use. Wearable, smartphones, and other mobile monitoring sensors are producing vast amounts of data that can be processed by deep learning algorithms in a variety of medical settings. Patients are eager for augmented medicine to be implemented because it will give them more control over their care and allow them to receive more tailored treatment, but doctors are hesitant to embrace the shift because they are not equipped to deal with the resulting changes in clinical practice. In addition, this phenomenon raises the questions of whether or not these cutting-edge tools should be validated by conventional clinical trials, whether or not medical schools should update their curricula to reflect the rise of digital medicine, and whether or not the ethics of constant connected monitoring should be taken into account. The purpose of this paper is to explore the current research literature and offer a holistic view of the implications of well-established clinical applications of artificial intelligence on medical professionals, hospitals, medical schools, and bioethics.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.260 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.116 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.493 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 · 5.438 Zit.