Data Characterization for Reliable AI in Medicine

被引:5
|
作者
Rajaraman, Sivaramakrishnan [1 ]
Zamzmi, Ghada [1 ]
Yang, Feng [1 ]
Xue, Zhiyun [1 ]
Antani, Sameer K. [1 ]
机构
[1] NIH, Bethesda, MD 20894 USA
来源
RECENT TRENDS IN IMAGE PROCESSING AND PATTERN RECOGNITION, RTIP2R 2022 | 2023年 / 1704卷
基金
美国国家卫生研究院;
关键词
Data characteristics; Artificial intelligence; Machine learning; Deep learning; Medical imaging; Data-driven design; Reliability; Generalizability; Robustness;
D O I
10.1007/978-3-031-23599-3_1
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Research in Artificial Intelligence (AI)-based medical computer vision algorithms bear promises to improve disease screening, diagnosis, and subsequently patient care. However, these algorithms are highly impacted by the characteristics of the underlying data. In this work, we discuss various data characteristics, namely Volume, Veracity, Validity, Variety, and Velocity, that impact the design, reliability, and evolution of machine learning in medical computer vision. Further, we discuss each characteristic and the recent works conducted in our research lab that informed our understanding of the impact of these characteristics on the design of medical decision-making algorithms and outcome reliability.
引用
收藏
页码:3 / 11
页数:9
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