31 citations · 62 across the 20 of their papers we have counts for
4 papers · 1 filter
Establishing Rigorous and Cost-effective Clinical Trials for Artificial Intelligence Models
Wanling Gao, Yunyou Huang, Dandan Cui +22
A profound gap persists between artificial intelligence (AI) and clinical practice in medicine, primarily due to the lack of rigorous and cost-effective evaluation methodologies. S…
OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical settings
Yunyou Huang, Nana Wang, Suqin Tang +10
This paper quantitatively reveals the state-of-the-art and state-of-the-practice AI systems only achieve acceptable performance on the stringent conditions that all categories of s…
AIBench Training: Balanced Industry-Standard AI Training Benchmarking
Fei Tang, Wanling Gao, Jianfeng Zhan +30
Earlier-stage evaluations of a new AI architecture/system need affordable benchmarks. Only using a few AI component benchmarks like MLPerfalone in the other stages may lead to misl…
A new direction to promote the implementation of artificial intelligence in natural clinical settings
Yunyou Huang, Zhifei Zhang, Nana Wang +4
Artificial intelligence (AI) researchers claim that they have made great `achievements' in clinical realms. However, clinicians point out the so-called `achievements' have no abili…