Fairness and Ethics in Artificial Intelligence-Based Medical Imagining

Subarna Tripathi, T. Musiolik
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引用次数: 7

Abstract

Artificial intelligence has a huge array of current and potential applications in healthcare and medicine. Ethical issues arising due to algorithmic biases are one of the greatest challenges faced in the generalizability of AI models today. The authors address safety and regulatory barriers that impede data sharing in medicine as well as potential changes to existing techniques and frameworks that might allow ethical data sharing for machine learning. With these developments in view, they also present different algorithmic models that are being used to develop machine learning-based medical systems that will potentially evolve to be free of the sample, annotator, and temporal bias. These AI-based medical imaging models will then be completely implemented in healthcare facilities and institutions all around the world, even in the remotest areas, making diagnosis and patient care both cheaper and freely accessible.
基于人工智能的医学想象中的公平与伦理
人工智能在医疗保健和医学领域有大量当前和潜在的应用。由算法偏差引起的伦理问题是当今人工智能模型泛化所面临的最大挑战之一。作者解决了阻碍医学数据共享的安全和监管障碍,以及对现有技术和框架的潜在改变,这些技术和框架可能允许机器学习的道德数据共享。考虑到这些发展,他们还提出了不同的算法模型,用于开发基于机器学习的医疗系统,这些系统可能会进化到没有样本、注释器和时间偏差。然后,这些基于人工智能的医学成像模型将在世界各地的医疗保健设施和机构中全面实施,甚至在最偏远的地区,使诊断和患者护理更加便宜和免费。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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