Explainable artificial intelligence methods in combating pandemics: A systematic review

F Giuste, W Shi, Y Zhu, T Naren, M Isgut… - IEEE Reviews in …, 2022‏ - ieeexplore.ieee.org
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 …

[HTML][HTML] Preliminary evidence of the use of generative AI in health care clinical services: systematic narrative review

D Yim, J Khuntia, V Parameswaran… - JMIR Medical …, 2024‏ - medinform.jmir.org
Background: Generative artificial intelligence tools and applications (GenAI) are being
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

J Yang, AAS Soltan, DW Eyre, DA Clifton - Nature Machine Intelligence, 2023‏ - nature.com
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 …

An adversarial training framework for mitigating algorithmic biases in clinical machine learning

J Yang, AAS Soltan, DW Eyre, Y Yang… - NPJ digital medicine, 2023‏ - nature.com
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 …

The importance of being external. methodological insights for the external validation of machine learning models in medicine

F Cabitza, A Campagner, F Soares… - Computer methods and …, 2021‏ - Elsevier
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 …

Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening

J Yang, AAS Soltan, DA Clifton - NPJ digital medicine, 2022‏ - nature.com
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 …

Clinical and laboratory approach to diagnose COVID-19 using machine learning

K Chadaga, C Chakraborty, S Prabhu… - Interdisciplinary …, 2022‏ - Springer
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 …

Develo** deep LSTMs with later temporal attention for predicting COVID-19 severity, clinical outcome, and antibody level by screening serological indicators over …

J Cai, Y Li, B Liu, Z Wu, S Zhu, Q Chen… - IEEE Journal of …, 2024‏ - ieeexplore.ieee.org
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 …

Generalizability assessment of AI models across hospitals in a low-middle and high income country

J Yang, NT Dung, PN Thach, NT Phong… - Nature …, 2024‏ - nature.com
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 …

A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening …

AAS Soltan, A Thakur, J Yang, A Chauhan… - The Lancet Digital …, 2024‏ - thelancet.com
Background Multicentre training could reduce biases in medical artificial intelligence (AI);
however, ethical, legal, and technical considerations can constrain the ability of hospitals to …