组织病理学
工作量
数字化病理学
客观性(哲学)
人工智能
癌症
计算机科学
医学物理学
一致性(知识库)
病理
医学
数据科学
内科学
哲学
认识论
操作系统
作者
Artem Shmatko,Narmin Ghaffari Laleh,Moritz Gerstung,Jakob Nikolas Kather
出处
期刊:Nature cancer
[Springer Nature]
日期:2022-09-22
卷期号:3 (9): 1026-1038
被引量:198
标识
DOI:10.1038/s43018-022-00436-4
摘要
Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative information from digital histopathology images. AI is expected to reduce workload for human experts, improve the objectivity and consistency of pathology reports, and have a clinical impact by extracting hidden information from routinely available data. Here, we describe how AI can be used to predict cancer outcome, treatment response, genetic alterations and gene expression from digitized histopathology slides. We summarize the underlying technologies and emerging approaches, noting limitations, including the need for data sharing and standards. Finally, we discuss the broader implications of AI in cancer research and oncology.
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