2019/05/08 by Yunyou Huang, Zhifei Zhang, Huang, Yunyou +11
Computer Science · Health Professions · Medicine · #Applications of artificial intelligence #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare #Artificial Intelligence in Healthcare and Education #Artificial intelligence #Benchmark (surveying) #Clinical Practice #Computer science #Data science #FOS: Computer and information sciences #Machine Learning in Healthcare #Medicine #Natural (archaeology) #Point (geometry) #Political science #Root (linguistics) #Suite #cs.AI
paper · pdf · doi:10.48550/arxiv.1905.02940
arxiv created 2019/05/08 · openalex publication_date 2019/05/08 · arxiv updated 2019/05/09 · openalex created_date 2019/05/16 · openalex updated_date 2026/07/28
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.