[HTML][HTML] The false hope of current approaches to explainable artificial intelligence in health care
The black-box nature of current artificial intelligence (AI) has caused some to question
whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has …
whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has …
[HTML][HTML] Opening the black box: the promise and limitations of explainable machine learning in cardiology
Many clinicians remain wary of machine learning because of longstanding concerns about
“black box” models.“Black box” is shorthand for models that are sufficiently complex that they …
“black box” models.“Black box” is shorthand for models that are sufficiently complex that they …
[HTML][HTML] Significance of machine learning in healthcare: Features, pillars and applications
Abstract Machine Learning (ML) applications are making a considerable impact on
healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the …
healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the …
Towards personalized federated learning
In parallel with the rapid adoption of artificial intelligence (AI) empowered by advances in AI
research, there has been growing awareness and concerns of data privacy. Recent …
research, there has been growing awareness and concerns of data privacy. Recent …
Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review
Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and
future potential for transforming almost all aspects of medicine. However, in many …
future potential for transforming almost all aspects of medicine. However, in many …
[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 …
Explainable artificial intelligence applications in cyber security: State-of-the-art in research
This survey presents a comprehensive review of current literature on Explainable Artificial
Intelligence (XAI) methods for cyber security applications. Due to the rapid development of …
Intelligence (XAI) methods for cyber security applications. Due to the rapid development of …
[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 …
Human–machine teaming is key to AI adoption: clinicians' experiences with a deployed machine learning system
While a growing number of machine learning (ML) systems have been deployed in clinical
settings with the promise of improving patient care, many have struggled to gain adoption …
settings with the promise of improving patient care, many have struggled to gain adoption …
" Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
Despite the proliferation of explainable AI (XAI) methods, little is understood about end-
users' explainability needs and behaviors around XAI explanations. To address this gap and …
users' explainability needs and behaviors around XAI explanations. To address this gap and …