Avoiding bias in artificial intelligence. Issue 3 (26th January 2023)

• Record Type: Journal Article
• Authors: Gudis, David A.; McCoul, Edward D.; Marino, Michael J.; Patel, Zara M.
• Is Part Of: International forum of allergy & rhinology. Volume 13:Issue 3(2023)
• Page Start: 193
• Page End: 195
• Publication Date: 2023-01-26
• Subjects: evidence‐based medicine -- imaging -- medical therapy of chronic rhinosinusitis -- patient‐reported outcome measure -- quality of life -- simulation training -- statistics; 617.51005
• DOI: 10.1002/alr.23129
• Languages: English
• ISSNs: 2042-6976

Abstract

Abstract: 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.