Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review
Abstract Transparency in Machine Learning (ML), often also referred to as interpretability or
explainability, attempts to reveal the working mechanisms of complex models. From a …
explainability, attempts to reveal the working mechanisms of complex models. From a …
Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model …
Purpose To recognize and address various sources of bias essential for algorithmic fairness
and trustworthiness and to contribute to a just and equitable deployment of AI in medical …
and trustworthiness and to contribute to a just and equitable deployment of AI in medical …
Deep learning in radiology: ethics of data and on the value of algorithm transparency, interpretability and explainability
A Fernandez-Quilez - AI and Ethics, 2023 - Springer
AI systems are quickly being adopted in radiology and, in general, in healthcare. A myriad of
systems is being proposed and developed on a daily basis for high-stake decisions that can …
systems is being proposed and developed on a daily basis for high-stake decisions that can …
FUTURE-AI: guiding principles and consensus recommendations for trustworthy artificial intelligence in medical imaging
The recent advancements in artificial intelligence (AI) combined with the extensive amount
of data generated by today's clinical systems, has led to the development of imaging AI …
of data generated by today's clinical systems, has led to the development of imaging AI …
Explainable deep learning models in medical image analysis
Deep learning methods have been very effective for a variety of medical diagnostic tasks
and have even outperformed human experts on some of those. However, the black-box …
and have even outperformed human experts on some of those. However, the black-box …
Designing clinically translatable artificial intelligence systems for high-dimensional medical imaging
Abstract The National Institutes of Health in 2018 identified key focus areas for the future of
artificial intelligence in medical imaging, creating a foundational roadmap for research in …
artificial intelligence in medical imaging, creating a foundational roadmap for research in …
Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency
Machine Intelligence (MI) is rapidly becoming an important approach across biomedical
discovery, clinical research, medical diagnostics/devices, and precision medicine. Such …
discovery, clinical research, medical diagnostics/devices, and precision medicine. Such …
Explainable ai for bioinformatics: Methods, tools and applications
Artificial intelligence (AI) systems utilizing deep neural networks and machine learning (ML)
algorithms are widely used for solving critical problems in bioinformatics, biomedical …
algorithms are widely used for solving critical problems in bioinformatics, biomedical …
[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] Democratizing artificial intelligence imaging analysis with automated machine learning: tutorial
AJ Thirunavukarasu, K Elangovan, L Gutierrez… - Journal of Medical …, 2023 - jmir.org
Deep learning–based clinical imaging analysis underlies diagnostic artificial intelligence
(AI) models, which can match or even exceed the performance of clinical experts, having the …
(AI) models, which can match or even exceed the performance of clinical experts, having the …