A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion

AS Albahri, AM Duhaim, MA Fadhel, A Alnoor… - Information …, 2023 - Elsevier
In the last few years, the trend in health care of embracing artificial intelligence (AI) has
dramatically changed the medical landscape. Medical centres have adopted AI applications …

Human-in-the-loop machine learning: a state of the art

E Mosqueira-Rey, E Hernández-Pereira… - Artificial Intelligence …, 2023 - Springer
Researchers are defining new types of interactions between humans and machine learning
algorithms generically called human-in-the-loop machine learning. Depending on who is in …

[HTML][HTML] Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

S Ali, T Abuhmed, S El-Sappagh, K Muhammad… - Information fusion, 2023 - Elsevier
Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

Machine learning for a sustainable energy future

Z Yao, Y Lum, A Johnston, LM Mejia-Mendoza… - Nature Reviews …, 2023 - nature.com
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …

Explainability for large language models: A survey

H Zhao, H Chen, F Yang, N Liu, H Deng, H Cai… - ACM Transactions on …, 2024 - dl.acm.org
Large language models (LLMs) have demonstrated impressive capabilities in natural
language processing. However, their internal mechanisms are still unclear and this lack of …

Survey of explainable AI techniques in healthcare

A Chaddad, J Peng, J Xu, A Bouridane - Sensors, 2023 - mdpi.com
Artificial intelligence (AI) with deep learning models has been widely applied in numerous
domains, including medical imaging and healthcare tasks. In the medical field, any judgment …

Explainable artificial intelligence: a comprehensive review

D Minh, HX Wang, YF Li, TN Nguyen - Artificial Intelligence Review, 2022 - Springer
Thanks to the exponential growth in computing power and vast amounts of data, artificial
intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be …

[HTML][HTML] Transparency of deep neural networks for medical image analysis: A review of interpretability methods

Z Salahuddin, HC Woodruff, A Chatterjee… - Computers in biology and …, 2022 - Elsevier
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 …

From attribution maps to human-understandable explanations through concept relevance propagation

R Achtibat, M Dreyer, I Eisenbraun, S Bosse… - Nature Machine …, 2023 - nature.com
The field of explainable artificial intelligence (XAI) aims to bring transparency to today's
powerful but opaque deep learning models. While local XAI methods explain individual …

[HTML][HTML] Notions of explainability and evaluation approaches for explainable artificial intelligence

G Vilone, L Longo - Information Fusion, 2021 - Elsevier
Abstract Explainable Artificial Intelligence (XAI) has experienced a significant growth over
the last few years. This is due to the widespread application of machine learning, particularly …