Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022)
Background and objectives Artificial intelligence (AI) has branched out to various
applications in healthcare, such as health services management, predictive medicine …
applications in healthcare, such as health services management, predictive medicine …
[HTML][HTML] Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …
grows, especially in high-stakes decision making areas such as medical image analysis …
RadImageNet: an open radiologic deep learning research dataset for effective transfer learning
Purpose To demonstrate the value of pretraining with millions of radiologic images
compared with ImageNet photographic images on downstream medical applications when …
compared with ImageNet photographic images on downstream medical applications when …
[HTML][HTML] Transparency of deep neural networks for medical image analysis: A review of interpretability methods
Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …
diagnosis and treatment decisions. Deep neural networks have shown the same or better …
[HTML][HTML] Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond
Abstract Explainable Artificial Intelligence (XAI) is an emerging research topic of machine
learning aimed at unboxing how AI systems' black-box choices are made. This research field …
learning aimed at unboxing how AI systems' black-box choices are made. This research field …
[HTML][HTML] Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
The past decade has seen significant progress in artificial intelligence (AI), which has
resulted in algorithms being adopted for resolving a variety of problems. However, this …
resulted in algorithms being adopted for resolving a variety of problems. However, this …
[HTML][HTML] A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta …
Background Deep learning offers considerable promise for medical diagnostics. We aimed
to evaluate the diagnostic accuracy of deep learning algorithms versus health-care …
to evaluate the diagnostic accuracy of deep learning algorithms versus health-care …
A governance model for the application of AI in health care
As the efficacy of artificial intelligence (AI) in improving aspects of healthcare delivery is
increasingly becoming evident, it becomes likely that AI will be incorporated in routine …
increasingly becoming evident, it becomes likely that AI will be incorporated in routine …
Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning
X Qian, J Pei, H Zheng, X Xie, L Yan, H Zhang… - Nature biomedical …, 2021 - nature.com
The clinical application of breast ultrasound for the assessment of cancer risk and of deep
learning for the classification of breast-ultrasound images has been hindered by inter-grader …
learning for the classification of breast-ultrasound images has been hindered by inter-grader …
On interpretability of artificial neural networks: A survey
Deep learning as performed by artificial deep neural networks (DNNs) has achieved great
successes recently in many important areas that deal with text, images, videos, graphs, and …
successes recently in many important areas that deal with text, images, videos, graphs, and …