Applications of explainable artificial intelligence in diagnosis and surgery
In recent years, artificial intelligence (AI) has shown great promise in medicine. However,
explainability issues make AI applications in clinical usages difficult. Some research has …
explainability issues make AI applications in clinical usages difficult. Some research has …
From blackbox to explainable AI in healthcare: existing tools and case studies
Introduction. Artificial intelligence (AI) models have been employed to automate decision‐
making, from commerce to more critical fields directly affecting human lives, including …
making, from commerce to more critical fields directly affecting human lives, including …
Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues
Abstract Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial
Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare …
Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare …
Federated learning for the internet-of-medical-things: A survey
Recently, in healthcare organizations, real-time data have been collected from connected or
implantable sensors, layered protocol stacks, lightweight communication frameworks, and …
implantable sensors, layered protocol stacks, lightweight communication frameworks, and …
Ensemble of 2D residual neural networks integrated with atrous spatial pyramid pooling module for myocardium segmentation of left ventricle cardiac MRI
Cardiac disease diagnosis and identification is problematic mostly by inaccurate
segmentation of the cardiac left ventricle (LV). Besides, LV segmentation is challenging …
segmentation of the cardiac left ventricle (LV). Besides, LV segmentation is challenging …
A brief review of explainable artificial intelligence in healthcare
Z Sadeghi, R Alizadehsani, MA Cifci, S Kausar… - ar** artificial intelligence models
and systems that can provide clear, understandable, and transparent explanations for their …
and systems that can provide clear, understandable, and transparent explanations for their …
Explainable artificial intelligence (XAI) in medical decision support systems (MDSS): applicability, prospects, legal implications, and challenges
The healthcare sector is very interested in machine learning (ML) and artificial intelligence
(AI). Nevertheless, applying AI applications in scientific contexts is difficult because of the …
(AI). Nevertheless, applying AI applications in scientific contexts is difficult because of the …
Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation
Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however,
the actual risk stratification models for haemorrhagic and thrombotic events are not validated …
the actual risk stratification models for haemorrhagic and thrombotic events are not validated …