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 …

[HTML][HTML] A systematic review of Explainable Artificial Intelligence models and applications: Recent developments and future trends

A Saranya, R Subhashini - Decision analytics journal, 2023 - Elsevier
Artificial Intelligence (AI) uses systems and machines to simulate human intelligence and
solve common real-world problems. Machine learning and deep learning are Artificial …

Trustworthy LLMs: A survey and guideline for evaluating large language models' alignment

Y Liu, Y Yao, JF Ton, X Zhang, RGH Cheng… - arXiv preprint arXiv …, 2023 - arxiv.org
Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

[HTML][HTML] Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade

SK Khare, S March, PD Barua, VM Gadre, UR Acharya - Information Fusion, 2023 - Elsevier
Mental health is a basic need for a sustainable and developing society. The prevalence and
financial burden of mental illness have increased globally, and especially in response to …

[HTML][HTML] Unveiling the dynamics of AI applications: A review of reviews using scientometrics and BERTopic modeling

R Raman, D Pattnaik, L Hughes… - Journal of Innovation & …, 2024 - Elsevier
In a world that has rapidly transformed through the advent of artificial intelligence (AI), our
systematic review, guided by the PRISMA protocol, investigates a decade of AI research …

Automated detection and forecasting of covid-19 using deep learning techniques: A review

A Shoeibi, M Khodatars, M Jafari, N Ghassemi… - Neurocomputing, 2024 - Elsevier
Abstract In March 2020, the World Health Organization (WHO) declared COVID-19 a global
epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real …

[HTML][HTML] An explainable and interpretable model for attention deficit hyperactivity disorder in children using EEG signals

SK Khare, UR Acharya - Computers in biology and medicine, 2023 - Elsevier
Background: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental
disorder that affects a person's sleep, mood, anxiety, and learning. Early diagnosis and …

[HTML][HTML] Adazd-Net: Automated adaptive and explainable Alzheimer's disease detection system using EEG signals

SK Khare, UR Acharya - Knowledge-Based Systems, 2023 - Elsevier
Background: Alzheimer's disease (AZD) is a degenerative neurological condition that
causes dementia and leads the brain to atrophy. Although AZD cannot be cured, early …

A Systematic Literature Review of Advancements, Challenges and Future Directions of AI And ML in Healthcare

GS Nadella, S Satish, K Meduri, SS Meduri - International Journal of …, 2023 - ijsdcs.com
The most remarkable advancements in artificial intelligence (AI) plus machine learning (ML)
technologies with integration addicted to healthcare systems face several challenges in …

Epilepsy detection in 121 patient populations using hypercube pattern from EEG signals

I Tasci, B Tasci, PD Barua, S Dogan, T Tuncer… - Information …, 2023 - Elsevier
Background Epilepsy is one of the most commonly seen neurologic disorders worldwide
and has generally caused seizures. Electroencephalography (EEG) is widely used in …