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 …
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 …
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 …
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 …
To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)
Artificial intelligence (AI) has made impressive progress over the past few years, including
many applications in medical imaging. Numerous commercial solutions based on AI …
many applications in medical imaging. Numerous commercial solutions based on AI …
Toward generalizability in the deployment of artificial intelligence in radiology: role of computation stress testing to overcome underspecification
T Eche, LH Schwartz, FZ Mokrane… - Radiology: Artificial …, 2021 - pubs.rsna.org
The clinical deployment of artificial intelligence (AI) applications in medical imaging is
perhaps the greatest challenge facing radiology in the next decade. One of the main …
perhaps the greatest challenge facing radiology in the next decade. One of the main …
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 …
“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 …
Assessment of radiology artificial intelligence software: a validation and evaluation framework
W Tanguay, P Acar, B Fine, M Abdolell… - Canadian …, 2023 - journals.sagepub.com
Artificial intelligence (AI) software in radiology is becoming increasingly prevalent and
performance is improving rapidly with new applications for given use cases being …
performance is improving rapidly with new applications for given use cases being …
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) …