[HTML][HTML] Learning disentangled representations in the imaging domain
Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …
general representations even in the absence of, or with limited, supervision. A good general …
Mitigating Racial And Ethnic Bias And Advancing Health Equity In Clinical Algorithms: A Scoping Review: Scoping review examines racial and ethnic bias in clinical …
In August 2022 the Department of Health and Human Services (HHS) issued a notice of
proposed rulemaking prohibiting covered entities, which include health care providers and …
proposed rulemaking prohibiting covered entities, which include health care providers and …
Generative models improve fairness of medical classifiers under distribution shifts
Abstract Domain generalization is a ubiquitous challenge for machine learning in
healthcare. Model performance in real-world conditions might be lower than expected …
healthcare. Model performance in real-world conditions might be lower than expected …
Demographic bias in misdiagnosis by computational pathology models
Despite increasing numbers of regulatory approvals, deep learning-based computational
pathology systems often overlook the impact of demographic factors on performance …
pathology systems often overlook the impact of demographic factors on performance …
Artificial intelligence in cardiac MRI: is clinical adoption forthcoming?
Artificial intelligence (AI) refers to the area of knowledge that develops computerised models
to perform tasks that typically require human intelligence. These algorithms are programmed …
to perform tasks that typically require human intelligence. These algorithms are programmed …
Understanding biases and disparities in radiology AI datasets: a review
Artificial intelligence (AI) continues to show great potential in disease detection and
diagnosis on medical imaging with increasingly high accuracy. An important component of …
diagnosis on medical imaging with increasingly high accuracy. An important component of …
Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging
A growing number of artificial intelligence (AI)-based systems are being proposed and
developed in cardiology, driven by the increasing need to deal with the vast amount of …
developed in cardiology, driven by the increasing need to deal with the vast amount of …
Fair machine learning in healthcare: A review
Benefiting from the digitization of healthcare data and the development of computing power,
machine learning methods are increasingly used in the healthcare domain. Fairness …
machine learning methods are increasingly used in the healthcare domain. Fairness …
Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population
S Yao, F Dai, P Sun, W Zhang, B Qian, H Lu - Nature Communications, 2024 - nature.com
Artificial Intelligence (AI) models for medical diagnosis often face challenges of
generalizability and fairness. We highlighted the algorithmic unfairness in a large thyroid …
generalizability and fairness. We highlighted the algorithmic unfairness in a large thyroid …
Towards advanced diagnosis and management of inherited arrhythmia syndromes: Harnessing the capabilities of artificial intelligence and machine learning
B Asatryan, H Bleijendaal, AAM Wilde - Heart Rhythm, 2023 - Elsevier
The use of advanced computational technologies, such as artificial intelligence (AI), is now
exerting a significant influence on various aspects of life, including healthcare and science …
exerting a significant influence on various aspects of life, including healthcare and science …