Accessing artificial intelligence for clinical decision-making
C Giordano, M Brennan, B Mohamed… - Frontiers in digital …, 2021 - frontiersin.org
Advancements in computing and data from the near universal acceptance and
implementation of electronic health records has been formative for the growth of …
implementation of electronic health records has been formative for the growth of …
Causal machine learning for healthcare and precision medicine
Causal machine learning (CML) has experienced increasing popularity in healthcare.
Beyond the inherent capabilities of adding domain knowledge into learning systems, CML …
Beyond the inherent capabilities of adding domain knowledge into learning systems, CML …
[HTML][HTML] Significance of machine learning in healthcare: Features, pillars and applications
Abstract Machine Learning (ML) applications are making a considerable impact on
healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the …
healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the …
[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 …
[HTML][HTML] Towards multi-modal causability with graph neural networks enabling information fusion for explainable AI
AI is remarkably successful and outperforms human experts in certain tasks, even in
complex domains such as medicine. Humans on the other hand are experts at multi-modal …
complex domains such as medicine. Humans on the other hand are experts at multi-modal …
Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications
Deep learning models have achieved high performance across different domains, such as
medical decision-making, autonomous vehicles, decision support systems, among many …
medical decision-making, autonomous vehicles, decision support systems, among many …
[HTML][HTML] Human-centered design to address biases in artificial intelligence
The potential of artificial intelligence (AI) to reduce health care disparities and inequities is
recognized, but it can also exacerbate these issues if not implemented in an equitable …
recognized, but it can also exacerbate these issues if not implemented in an equitable …
[HTML][HTML] The explainability paradox: Challenges for xAI in digital pathology
The increasing prevalence of digitised workflows in diagnostic pathology opens the door to
life-saving applications of artificial intelligence (AI). Explainability is identified as a critical …
life-saving applications of artificial intelligence (AI). Explainability is identified as a critical …
Simulation intelligence: Towards a new generation of scientific methods
The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …
computing, where a motif is an algorithmic method that captures a pattern of computation …
The next frontier: AI we can really trust
A Holzinger - Joint European conference on machine learning and …, 2021 - Springer
Enormous advances in the domain of statistical machine learning, the availability of large
amounts of training data, and increasing computing power have made Artificial Intelligence …
amounts of training data, and increasing computing power have made Artificial Intelligence …