.png){kind=icon}
Return
Avoiding bias in artificial intelligence
.png){kind=icon}
PRE
AI
DOI：10.1002/alr.23129.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Artificial intelligence (AI) is ubiquitous and expanding, and the healthcare industry has rapidly adopted AI and machine learning for numerous applications. It is essential to understand that AI is not immune to the biases that impact our clinical and academic work, and in fact may inadvertently amplify rather than reduce them. As we harness the power of AI, it is our obligation to our patients to ensure that we address these concerns. We must take responsibility for proactive stewardship to protect against bias, not only for new AI algorithms, but also for our research studies that may one day provide data for those algorithms.
Keywords:
evidence-based medicine
imaging
medical therapy of chronic rhinosinusitis
patient-reported outcome measure
quality of life
simulation training
statistics
Journal
IF:
6.8
Papers:
2.6K
Citations:
5.8K
Organization
View more organizations
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSaveShareSave
no more