Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
The need for quantification of uncertainty in artificial intelligence for clinical data analysis: increasing the level of trust in the decision-making process
44
Zitationen
5
Autoren
2022
Jahr
Abstract
Different terms such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">trust</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">certainty</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">uncertainty</i> are of great importance in the real world and play a critical role in artificial intelligence (AI) applications. The implied assumption is that the level of trust in AI can be measured in different ways. This principle can be achieved by distinguishing uncertainties in predicting AI methods used in medical studies. Hence, it is necessary to propose effective uncertainty quantification (UQ) and measurement methods to have trustworthy AI (TAI) clinical decision support systems (CDSSs). In this study, we present practical guidelines for developing and using UQ methods while applying various AI techniques for medical data analysis.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.245 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.102 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.468 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 · 5.429 Zit.