Understanding and mitigating bias in imaging artificial intelligence
Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model
development, with potential for exacerbating health disparities. However, bias in imaging AI …
development, with potential for exacerbating health disparities. However, bias in imaging AI …
Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects
Although artificial intelligence (AI) methods hold promise for medical imaging-based
prediction tasks, their integration into medical practice may present a double-edged sword …
prediction tasks, their integration into medical practice may present a double-edged sword …
Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging
Objective Artificial intelligence (AI) models trained using medical images for clinical tasks
often exhibit bias in the form of subgroup performance disparities. However, since not all …
often exhibit bias in the form of subgroup performance disparities. However, since not all …
Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations
Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be
practiced in the near future, but several issues need to be resolved before AI can be widely …
practiced in the near future, but several issues need to be resolved before AI can be widely …
Mitigating bias in radiology machine learning: 2. Model development
There are increasing concerns about the bias and fairness of artificial intelligence (AI)
models as they are put into clinical practice. Among the steps for implementing machine …
models as they are put into clinical practice. Among the steps for implementing machine …
A road map for translational research on artificial intelligence in medical imaging: from the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop
B Allen Jr, SE Seltzer, CP Langlotz, KP Dreyer… - Journal of the American …, 2019 - Elsevier
Advances in machine learning in medical imaging are occurring at a rapid pace in research
laboratories both at academic institutions and in industry. Important artificial intelligence (AI) …
laboratories both at academic institutions and in industry. Important artificial intelligence (AI) …
“Shortcuts” causing bias in radiology artificial intelligence: causes, evaluation and mitigation.
Despite the expert-level performance of AI models for various medical imaging tasks, real-
world performance failures with disparate outputs for various subgroups limit the usefulness …
world performance failures with disparate outputs for various subgroups limit the usefulness …
AI pitfalls and what not to do: mitigating bias in AI
Various forms of artificial intelligence (AI) applications are being deployed and used in many
healthcare systems. As the use of these applications increases, we are learning the failures …
healthcare systems. As the use of these applications increases, we are learning the failures …
Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success
Worldwide interest in artificial intelligence (AI) applications, including imaging, is high and
growing rapidly, fueled by availability of large datasets (“big data”), substantial advances in …
growing rapidly, fueled by availability of large datasets (“big data”), substantial advances in …
[引用][C] Re: Guidance For Regulation Of Artificial Intelligence Applications
RT Vought - 2020 - acr.org
The American College of Radiology (ACR)—a professional association representing nearly
40,000 diagnostic radiologists, interventional radiologists, nuclear medicine physicians …
40,000 diagnostic radiologists, interventional radiologists, nuclear medicine physicians …