A causal perspective on dataset bias in machine learning for medical imaging
As machine learning methods gain prominence within clinical decision-making, the need to
address fairness concerns becomes increasingly urgent. Despite considerable work …
address fairness concerns becomes increasingly urgent. Despite considerable work …
No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging
As machine learning methods gain prominence within clinical decision-making, addressing
fairness concerns becomes increasingly urgent. Despite considerable work dedicated to …
fairness concerns becomes increasingly urgent. Despite considerable work dedicated to …
MEDFAIR: benchmarking fairness for medical imaging
A multitude of work has shown that machine learning-based medical diagnosis systems can
be biased against certain subgroups of people. This has motivated a growing number of …
be biased against certain subgroups of people. This has motivated a growing number of …
Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model …
Purpose To recognize and address various sources of bias essential for algorithmic fairness
and trustworthiness and to contribute to a just and equitable deployment of AI in medical …
and trustworthiness and to contribute to a just and equitable deployment of AI in medical …
Model selection's disparate impact in real-world deep learning applications
Algorithmic fairness has emphasized the role of biased data in automated decision
outcomes. Recently, there has been a shift in attention to sources of bias that implicate …
outcomes. Recently, there has been a shift in attention to sources of bias that implicate …
[HTML][HTML] Addressing fairness in artificial intelligence for medical imaging
A plethora of work has shown that AI systems can systematically and unfairly be biased
against certain populations in multiple scenarios. The field of medical imaging, where AI …
against certain populations in multiple scenarios. The field of medical imaging, where AI …
[HTML][HTML] Not all biases are bad: equitable and inequitable biases in machine learning and radiology
M Pot, N Kieusseyan, B Prainsack - Insights into imaging, 2021 - Springer
The application of machine learning (ML) technologies in medicine generally but also in
radiology more specifically is hoped to improve clinical processes and the provision of …
radiology more specifically is hoped to improve clinical processes and the provision of …
The Limits of Fair Medical Imaging AI In The Wild
As artificial intelligence (AI) rapidly approaches human-level performance in medical
imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Prior …
imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Prior …
[HTML][HTML] 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 …
[HTML][HTML] Improving model fairness in image-based computer-aided diagnosis
Deep learning has become a popular tool for computer-aided diagnosis using medical
images, sometimes matching or exceeding the performance of clinicians. However, these …
images, sometimes matching or exceeding the performance of clinicians. However, these …