Over the past few years, Artificial Intelligence (AI), and more specifically, Machine Learning (ML) technology, have experienced rapid adoption in the healthcare space as tools for diagnosis and decision-making. Such tools are intended to address challenges in the healthcare system to both processes and put into practice the proliferating medical findings, and also to support delivery of the promise of personalized and precision medicine.
Unintended bias in AI and AI-driven healthcare applications is an evolving topic that developers, reviewers, and experts are still learning to address effectively and consistently. The Good Machine Learning Practices Working Team of the AFDO/RAPS Healthcare Products Collaborative believes the topic of bias could benefit from standard taxonomy, consistent approaches to identification, and addressing any identified sources of bias.
在過去幾年中，人工智能 (AI)，更具體地說是機器學習 (ML) 技術，作為診斷和決策工具在醫療保健領域得到了迅速採用。這些工具旨在應對醫療保健系統中對流程和實踐不斷增長的醫學發現的挑戰，同時也支持實現個性化和精準醫療的承諾。
AI 和 AI 驅動的醫療保健應用程序中的意外偏見是一個不斷發展的話題，開發人員、審閱者和專家仍在學習有效和一致地解決這個問題。AFDO/RAPS Healthcare Products Collaborative 的 Good Machine Learning Practices Working Team 認為偏見主題可以從標準分類法、一致的識別方法和解決任何已識別的偏見來源中獲益。
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