Remote patient monitoring using artificial intelligence: Current state, applications, and challenges
The adoption of artificial intelligence (AI) in healthcare is growing rapidly. Remote patient
monitoring (RPM) is one of the common healthcare applications that assist doctors to …
monitoring (RPM) is one of the common healthcare applications that assist doctors to …
[HTML][HTML] Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)
Uncertainty estimation in healthcare involves quantifying and understanding the inherent
uncertainty or variability associated with medical predictions, diagnoses, and treatment …
uncertainty or variability associated with medical predictions, diagnoses, and treatment …
Towards trustworthy rotating machinery fault diagnosis via attention uncertainty in transformer
To enable researchers to fully trust the decisions made by deep diagnostic models,
interpretable rotating machinery fault diagnosis (RMFD) research has emerged. Existing …
interpretable rotating machinery fault diagnosis (RMFD) research has emerged. Existing …
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
Neural networks (NNs) are currently changing the computational paradigm on how to
combine data with mathematical laws in physics and engineering in a profound way …
combine data with mathematical laws in physics and engineering in a profound way …
Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles
Recent intelligent fault diagnosis technologies can effectively identify the machinery health
condition, while they are learnt based on a closed-world assumption, ie, the training and …
condition, while they are learnt based on a closed-world assumption, ie, the training and …
Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework
Fault diagnosis is efficient to improve the safety, reliability, and cost-effectiveness of
industrial machinery. Deep learning has been extensively investigated in fault diagnosis …
industrial machinery. Deep learning has been extensively investigated in fault diagnosis …
Rambo-rl: Robust adversarial model-based offline reinforcement learning
Offline reinforcement learning (RL) aims to find performant policies from logged data without
further environment interaction. Model-based algorithms, which learn a model of the …
further environment interaction. Model-based algorithms, which learn a model of the …
Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms
The task of empowering large language models (LLMs) to accurately express their
confidence, referred to as confidence elicitation, is essential in ensuring reliable and …
confidence, referred to as confidence elicitation, is essential in ensuring reliable and …
Trufor: Leveraging all-round clues for trustworthy image forgery detection and localization
In this paper we present TruFor, a forensic framework that can be applied to a large variety
of image manipulation methods, from classic cheapfakes to more recent manipulations …
of image manipulation methods, from classic cheapfakes to more recent manipulations …
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …