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[HTML][HTML] The enlightening role of explainable artificial intelligence in medical & healthcare domains: A systematic literature review
S Ali, F Akhlaq, AS Imran, Z Kastrati… - Computers in Biology …, 2023 - Elsevier
In domains such as medical and healthcare, the interpretability and explainability of
machine learning and artificial intelligence systems are crucial for building trust in their …
machine learning and artificial intelligence systems are crucial for building trust in their …
[HTML][HTML] A sco** review of interpretability and explainability concerning artificial intelligence methods in medical imaging
Abstract Purpose To review eXplainable Artificial Intelligence/(XAI) methods available for
medical imaging/(MI). Method A sco** review was conducted following the Joanna Briggs …
medical imaging/(MI). Method A sco** review was conducted following the Joanna Briggs …
Chest X-ray classification for the detection of COVID-19 using deep learning techniques
E Khan, MZU Rehman, F Ahmed, FA Alfouzan… - Sensors, 2022 - mdpi.com
Recent technological developments pave the path for deep learning-based techniques to be
used in almost every domain of life. The precision of deep learning techniques make it …
used in almost every domain of life. The precision of deep learning techniques make it …
Interpretable medical imagery diagnosis with self-attentive transformers: a review of explainable AI for health care
T Lai - BioMedInformatics, 2024 - mdpi.com
Recent advancements in artificial intelligence (AI) have facilitated its widespread adoption in
primary medical services, addressing the demand–supply imbalance in healthcare. Vision …
primary medical services, addressing the demand–supply imbalance in healthcare. Vision …
Learning autoencoder ensembles for detecting malware hidden communications in IoT ecosystems
N Cassavia, L Caviglione, M Guarascio… - Journal of Intelligent …, 2024 - Springer
Modern IoT ecosystems are the preferred target of threat actors wanting to incorporate
resource-constrained devices within a botnet or leak sensitive information. A major research …
resource-constrained devices within a botnet or leak sensitive information. A major research …
Untangling classification methods for melanoma skin cancer
A Kumar, A Vatsa - Frontiers in big Data, 2022 - frontiersin.org
Skin cancer is the most common cancer in the USA, and it is a leading cause of death
worldwide. Every year, more than five million patients are newly diagnosed in the USA. The …
worldwide. Every year, more than five million patients are newly diagnosed in the USA. The …
Explainable NLP model for predicting patient admissions at emergency department using triage notes
E Arnaud, M Elbattah… - … Conference on Big …, 2023 - ieeexplore.ieee.org
Explainable Artificial Intelligence (XAI) has the potential to revolutionize healthcare by
providing more transparent, trustworthy, and understandable predictions made by AI …
providing more transparent, trustworthy, and understandable predictions made by AI …
Explainable computational intelligence model for antepartum fetal monitoring to predict the risk of IUGR
Intrauterine Growth Restriction (IUGR) is a restriction of the fetus that involves the abnormal
growth rate of the fetus, and it has a huge impact on the new-born's health. Machine learning …
growth rate of the fetus, and it has a huge impact on the new-born's health. Machine learning …
[HTML][HTML] Hypertension diagnosis with backpropagation neural networks for sustainability in public health
JA Orozco Torres, A Medina Santiago… - Sensors, 2022 - mdpi.com
This paper presents the development of a multilayer feed-forward neural network for the
diagnosis of hypertension, based on a population-based study. For the development of this …
diagnosis of hypertension, based on a population-based study. For the development of this …
Human-In-The-Loop machine learning for the treatment of pancreatic cancer
E Mosqueira-Rey, A Pérez-Sánchez… - … Joint Conference on …, 2023 - ieeexplore.ieee.org
Human-in-the-Loop Machine Learning (HITL-ML) is a set of techniques that attempt to
actively introduce experts into the learning loop of machine learning (ML) models to improve …
actively introduce experts into the learning loop of machine learning (ML) models to improve …