Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review
Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and
future potential for transforming almost all aspects of medicine. However, in many …
future potential for transforming almost all aspects of medicine. However, in many …
A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts
G Schwalbe, B Finzel - Data Mining and Knowledge Discovery, 2024 - Springer
In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation
criteria have been developed within the research field of explainable artificial intelligence …
criteria have been developed within the research field of explainable artificial intelligence …
[HTML][HTML] Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions
Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …
[HTML][HTML] Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence
Medical artificial intelligence (AI) systems have been remarkably successful, even
outperforming human performance at certain tasks. There is no doubt that AI is important to …
outperforming human performance at certain tasks. There is no doubt that AI is important to …
Explainable AI for clinical and remote health applications: a survey on tabular and time series data
F Di Martino, F Delmastro - Artificial Intelligence Review, 2023 - Springer
Abstract Nowadays Artificial Intelligence (AI) has become a fundamental component of
healthcare applications, both clinical and remote, but the best performing AI systems are …
healthcare applications, both clinical and remote, but the best performing AI systems are …
Explainable artificial intelligence for tabular data: A survey
Machine learning techniques are increasingly gaining attention due to their widespread use
in various disciplines across academia and industry. Despite their tremendous success …
in various disciplines across academia and industry. Despite their tremendous success …
From" where" to" what": Towards human-understandable explanations through concept relevance propagation
The emerging field of eXplainable Artificial Intelligence (XAI) aims to bring transparency to
today's powerful but opaque deep learning models. While local XAI methods explain …
today's powerful but opaque deep learning models. While local XAI methods explain …
Beyond supervised learning for pervasive healthcare
The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
Trustworthy AI guidelines in biomedical decision-making applications: a scoping review
M Mora-Cantallops, E García-Barriocanal… - Big Data and Cognitive …, 2024 - mdpi.com
Recently proposed legal frameworks for Artificial Intelligence (AI) depart from some
frameworks of concepts regarding ethical and trustworthy AI that provide the technical …
frameworks of concepts regarding ethical and trustworthy AI that provide the technical …
Explainable and interpretable machine learning and data mining
The growing number of applications of machine learning and data mining in many domains—
from agriculture to business, education, industrial manufacturing, and medicine—gave rise …
from agriculture to business, education, industrial manufacturing, and medicine—gave rise …