[HTML][HTML] Artificial intelligence (AI) and internet of medical things (IoMT) assisted biomedical systems for intelligent healthcare
P Manickam, SA Mariappan, SM Murugesan, S Hansda… - Biosensors, 2022 - mdpi.com
Artificial intelligence (AI) is a modern approach based on computer science that develops
programs and algorithms to make devices intelligent and efficient for performing tasks that …
programs and algorithms to make devices intelligent and efficient for performing tasks that …
Artificial intelligence in healthcare: past, present and future
Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm
shift to healthcare, powered by increasing availability of healthcare data and rapid progress …
shift to healthcare, powered by increasing availability of healthcare data and rapid progress …
What is machine learning? A primer for the epidemiologist
Q Bi, KE Goodman, J Kaminsky… - American journal of …, 2019 - academic.oup.com
Abstract Machine learning is a branch of computer science that has the potential to transform
epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new …
epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new …
[HTML][HTML] Human-like problem-solving abilities in large language models using ChatGPT
Backgrounds The field of Artificial Intelligence (AI) has seen a major shift in recent years due
to the development of new Machine Learning (ML) models such as Generative Pre-trained …
to the development of new Machine Learning (ML) models such as Generative Pre-trained …
[HTML][HTML] Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning
Recent critical commentaries unfavorably compare deep learning (DL) with standard
machine learning (SML) approaches for brain imaging data analysis. However, their …
machine learning (SML) approaches for brain imaging data analysis. However, their …
Building better biomarkers: brain models in translational neuroimaging
Despite its great promise, neuroimaging has yet to substantially impact clinical practice and
public health. However, a developing synergy between emerging analysis techniques and …
public health. However, a developing synergy between emerging analysis techniques and …
Notice of retraction: AI techniques for COVID-19
AA Hussain, O Bouachir, F Al-Turjman… - IEEE access, 2020 - ieeexplore.ieee.org
Notice of Retraction "AI Techniques for COVID-19," by Adedoyin Ahmed Hussain; Ouns
Bouachir; Fadi Al-Turjman; Moayad A Page 1 Notice of Retraction "AI Techniques for COVID-19," …
Bouachir; Fadi Al-Turjman; Moayad A Page 1 Notice of Retraction "AI Techniques for COVID-19," …
[HTML][HTML] Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications
Deep learning (DL) is a family of machine learning methods that has gained considerable
attention in the scientific community, breaking benchmark records in areas such as speech …
attention in the scientific community, breaking benchmark records in areas such as speech …
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …
attention in recent years. Using a variety of neuroimaging modalities such as structural …
[HTML][HTML] Cognitive neuroscience of attention deficit hyperactivity disorder (ADHD) and its clinical translation
K Rubia - Frontiers in human neuroscience, 2018 - frontiersin.org
This review focuses on the cognitive neuroscience of Attention Deficit Hyperactivity Disorder
(ADHD) based on functional magnetic resonance imaging (fMRI) studies and on recent …
(ADHD) based on functional magnetic resonance imaging (fMRI) studies and on recent …