复杂度
医学
神经影像学
人工智能
数据科学
领域(数学)
计算机科学
精神科
数学
社会科学
社会学
纯数学
作者
Donna J. Cross,Seisaku Komori,Satoshi Minoshima
出处
期刊:Pet Clinics
[Elsevier BV]
日期:2021-11-20
卷期号:17 (1): 57-64
被引量:4
标识
DOI:10.1016/j.cpet.2021.08.001
摘要
AI has been applied to brain molecular imaging for over 30 years. The past two decades, have seen explosive progress. AI applications span from operations processes such as attenuation correction and image generation, to disease diagnosis and prediction. As sophistication in AI software platforms increases, and the availability of large imaging data repositories become common, future studies will incorporate more multidimensional datasets and information that may truly reach "superhuman" levels in the field of brain imaging. However, even with a growing level of complexity, these advanced networks will still require human supervision for appropriate application and interpretation in medical practice.
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