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Explainable artificial intelligence methods in combating pandemics: A systematic review
Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-
based solutions to COVID-19 challenges during the pandemic, few have made a significant …
based solutions to COVID-19 challenges during the pandemic, few have made a significant …
[HTML][HTML] Preliminary evidence of the use of generative AI in health care clinical services: systematic narrative review
Background: Generative artificial intelligence tools and applications (GenAI) are being
increasingly used in health care. Physicians, specialists, and other providers have started …
increasingly used in health care. Physicians, specialists, and other providers have started …
Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning
As models based on machine learning continue to be developed for healthcare applications,
greater effort is needed to ensure that these technologies do not reflect or exacerbate any …
greater effort is needed to ensure that these technologies do not reflect or exacerbate any …
An adversarial training framework for mitigating algorithmic biases in clinical machine learning
Abstract Machine learning is becoming increasingly prominent in healthcare. Although its
benefits are clear, growing attention is being given to how these tools may exacerbate …
benefits are clear, growing attention is being given to how these tools may exacerbate …
The importance of being external. methodological insights for the external validation of machine learning models in medicine
Abstract Background and Objective Medical machine learning (ML) models tend to perform
better on data from the same cohort than on new data, often due to overfitting, or co-variate …
better on data from the same cohort than on new data, often due to overfitting, or co-variate …
Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening
As patient health information is highly regulated due to privacy concerns, most machine
learning (ML)-based healthcare studies are unable to test on external patient cohorts …
learning (ML)-based healthcare studies are unable to test on external patient cohorts …
Clinical and laboratory approach to diagnose COVID-19 using machine learning
Abstract Coronavirus 2 (SARS-CoV-2), often known by the name COVID-19, is a type of
acute respiratory syndrome that has had a significant influence on both economy and health …
acute respiratory syndrome that has had a significant influence on both economy and health …
Develo** deep LSTMs with later temporal attention for predicting COVID-19 severity, clinical outcome, and antibody level by screening serological indicators over …
Objective: The clinical course of COVID-19, as well as the immunological reaction, is notable
for its extreme variability. Identifying the main associated factors might help understand the …
for its extreme variability. Identifying the main associated factors might help understand the …
Generalizability assessment of AI models across hospitals in a low-middle and high income country
The integration of artificial intelligence (AI) into healthcare systems within low-middle income
countries (LMICs) has emerged as a central focus for various initiatives aiming to improve …
countries (LMICs) has emerged as a central focus for various initiatives aiming to improve …
A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening …
Background Multicentre training could reduce biases in medical artificial intelligence (AI);
however, ethical, legal, and technical considerations can constrain the ability of hospitals to …
however, ethical, legal, and technical considerations can constrain the ability of hospitals to …