Bias mitigation for machine learning classifiers: A comprehensive survey
This article provides a comprehensive survey of bias mitigation methods for achieving
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …
Risk of bias in chest radiography deep learning foundation models
Purpose To analyze a recently published chest radiography foundation model for the
presence of biases that could lead to subgroup performance disparities across biologic sex …
presence of biases that could lead to subgroup performance disparities across biologic sex …
Human visual explanations mitigate bias in AI-based assessment of surgeon skills
Artificial intelligence (AI) systems can now reliably assess surgeon skills through videos of
intraoperative surgical activity. With such systems informing future high-stakes decisions …
intraoperative surgical activity. With such systems informing future high-stakes decisions …
Fairclip: Harnessing fairness in vision-language learning
Fairness is a critical concern in deep learning especially in healthcare where these models
influence diagnoses and treatment decisions. Although fairness has been investigated in the …
influence diagnoses and treatment decisions. Although fairness has been investigated in the …
Improving fairness in ai models on electronic health records: The case for federated learning methods
Developing AI tools that preserve fairness is of critical importance, specifically in high-stakes
applications such as those in healthcare. However, health AI models' overall prediction …
applications such as those in healthcare. However, health AI models' overall prediction …
Fairness metrics for health AI: we have a long way to go
The use of Artificial Intelligence (AI) is on track to revolutionize healthcare, with performance
in medical tasks such as clinical diagnosis often being comparable to expert-level accuracy …
in medical tasks such as clinical diagnosis often being comparable to expert-level accuracy …
Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest X-ray Classification Using Social Determinants of …
D Moukheiber, S Mahindre, L Moukheiber… - arXiv preprint arXiv …, 2024 - arxiv.org
There has been significant progress in implementing deep learning models in disease
diagnosis using chest X-rays. Despite these advancements, inherent biases in these models …
diagnosis using chest X-rays. Despite these advancements, inherent biases in these models …
Towards objective and systematic evaluation of bias in medical imaging AI
Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit
bias in the form of disparities in performance between subgroups. Since not all sources of …
bias in the form of disparities in performance between subgroups. Since not all sources of …
A Flexible Framework for Simulating and Evaluating Biases in Deep Learning-Based Medical Image Analysis
Despite the remarkable advances in deep learning for medical image analysis, it has
become evident that biases in datasets used for training such models pose considerable …
become evident that biases in datasets used for training such models pose considerable …
Analyzing the Impact of Personalization on Fairness in Federated Learning for Healthcare
As machine learning (ML) usage becomes more popular in the healthcare sector, there are
also increasing concerns about potential biases and risks such as privacy. One …
also increasing concerns about potential biases and risks such as privacy. One …