A new direction to promote the implementation of artificial intelligence in natural clinical settings

Huang, Yunyou, Zhang, Zhifei, Wang, Nana, Li, Nengquan, Du, Mengjia, Hao, Tianshu, Zhan, Jianfeng

arXiv.org Artificial Intelligence 

These authors contributed equally to this work. Artificial intelligence (AI) researchers claim that they have made great'achievements' in clinical realms. However, clinicians point out the so-called'achievements' have no ability to implement into natural clinical settings. The root cause for this huge gap is that many essential features of natural clinical tasks are overlooked by AI system developers without medical background. In this paper, we propose that the clinical benchmark suite is a novel and promising direction to capture the essential features of the real-world clinical tasks, hence qualifies itself for guiding the development of AI systems, promoting the implementation of AI in real-world clinical practice. AI researchers claim that they have obtained many significant'achievements' in various However, in practice, most of the AI products fail to obtain approval from the Food and Drug Administration (FDA). AI devices are not qualified handling high-risk tasks such as clinical diagnosis .

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