[HTML][HTML] As if sand were stone. New concepts and metrics to probe the ground on which to build trustable AI
Background We focus on the importance of interpreting the quality of the labeling used as
the input of predictive models to understand the reliability of their output in support of human …
the input of predictive models to understand the reliability of their output in support of human …
[HTML][HTML] The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and …
Artificial intelligence (AI) has huge potential to improve the health and well-being of people,
but adoption in clinical practice is still limited. Lack of transparency is identified as one of the …
but adoption in clinical practice is still limited. Lack of transparency is identified as one of the …
Trustworthy medical AI systems need to know when they don't know
T Grote - Journal of medical ethics, 2021 - jme.bmj.com
There is much to learn from Durán and Jongsma's paper. 1 One particularly important insight
concerns the relationship between epistemology and ethics in medical artificial intelligence …
concerns the relationship between epistemology and ethics in medical artificial intelligence …
Hurdles to artificial intelligence deployment: noise in schemas and “gold” labels
Despite frequent reports of imaging artificial intelligence (AI) that parallels human
performance, clinicians often question the safety and robustness of AI products in practice …
performance, clinicians often question the safety and robustness of AI products in practice …
Interpretable AI in healthcare: Enhancing fairness, safety, and trust
The value and future potentials of AI in healthcare are becoming self-evident, presenting an
escalating body of evidence. However, the adoption into clinical practice is still significantly …
escalating body of evidence. However, the adoption into clinical practice is still significantly …
[HTML][HTML] Explainable machine learning practices: opening another black box for reliable medical AI
In the past few years, machine learning (ML) tools have been implemented with success in
the medical context. However, several practitioners have raised concerns about the lack of …
the medical context. However, several practitioners have raised concerns about the lack of …
[HTML][HTML] Evaluating pointwise reliability of machine learning prediction
Abstract Interest in Machine Learning applications to tackle clinical and biological problems
is increasing. This is driven by promising results reported in many research papers, the …
is increasing. This is driven by promising results reported in many research papers, the …
Trust metrics for medical deep learning using explainable-ai ensemble for time series classification
K Siddiqui, TE Doyle - 2022 IEEE Canadian Conference on …, 2022 - ieeexplore.ieee.org
Trustworthiness is a roadblock in mass adoption of artificial intelligence (AI) in medicine.
This research developed a framework to explore the trustworthiness as it applies to AI in …
This research developed a framework to explore the trustworthiness as it applies to AI in …
[HTML][HTML] The elephant in the machine: Proposing a new metric of data reliability and its application to a medical case to assess classification reliability
F Cabitza, A Campagner, D Albano, A Aliprandi… - Applied Sciences, 2020 - mdpi.com
In this paper, we present and discuss a novel reliability metric to quantify the extent a ground
truth, generated in multi-rater settings, as a reliable basis for the training and validation of …
truth, generated in multi-rater settings, as a reliable basis for the training and validation of …
Explainable AI applications in the Medical Domain: a systematic review
Artificial Intelligence in Medicine has made significant progress with emerging applications
in medical imaging, patient care, and other areas. While these applications have proven …
in medical imaging, patient care, and other areas. While these applications have proven …