Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
Deep learning (DL) has the potential to transform medical diagnostics. However, the
diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of …
diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of …
Artificial intelligence in healthcare: review, ethics, trust challenges & future research directions
The use of artificial intelligence (AI) in medicine is beginning to alter current procedures in
prevention, diagnosis, treatment, amelioration, cure of disease and other physical and …
prevention, diagnosis, treatment, amelioration, cure of disease and other physical and …
[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 …
Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions
The way people travel, organise their time, and acquire information has changed due to
information technologies. Artificial intelligence (AI) and machine learning (ML) are …
information technologies. Artificial intelligence (AI) and machine learning (ML) are …
Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study
Data privacy mechanisms are essential for rapidly scaling medical training databases to
capture the heterogeneity of patient data distributions toward robust and generalizable …
capture the heterogeneity of patient data distributions toward robust and generalizable …
[HTML][HTML] Role of artificial intelligence applications in real-life clinical practice: systematic review
Background Artificial intelligence (AI) applications are growing at an unprecedented pace in
health care, including disease diagnosis, triage or screening, risk analysis, surgical …
health care, including disease diagnosis, triage or screening, risk analysis, surgical …
A time-resolved proteomic and prognostic map of COVID-19
V Demichev, P Tober-Lau, O Lemke, T Nazarenko… - Cell systems, 2021 - cell.com
COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection
to severe organ damage and death. We characterized the time-dependent progression of …
to severe organ damage and death. We characterized the time-dependent progression of …
COVID-Classifier: An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images
Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19
patients with pneumonia. However, the similarity between features of CXR images of COVID …
patients with pneumonia. However, the similarity between features of CXR images of COVID …
Securing internet of medical things systems: Limitations, issues and recommendations
Traditional health-care systems suffer from new challenges associated with the constant
increase in the number of patients. In order to address this issue, and to increase the …
increase in the number of patients. In order to address this issue, and to increase the …
Trends in using IoT with machine learning in health prediction system
A Aldahiri, B Alrashed, W Hussain - Forecasting, 2021 - mdpi.com
Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things
(IoT) data. These hybrid technologies work smartly to improve the decision-making process …
(IoT) data. These hybrid technologies work smartly to improve the decision-making process …