Harnessing oncology real-world data with AI

被引：0
|
作者：
Piers Mahon
Geoff Hall
Andre Dekker
Janne Vehreschild
Giovanni Tonon
机构：
[1] Digital Institute for Cancer Outcomes Research E.E.I.G,Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction
[2] IQVIA Cancer Research BV,undefined
[3] Leeds Teaching Hospital NHS Trust,undefined
[4] Health Data Research,undefined
[5] Maastricht University Medical Centre,undefined
[6] University Hospital of Frankfurt,undefined
[7] Center for Internal Medicine,undefined
[8] Department II — Hematology/Oncology,undefined
[9] Center for Omics Sciences,undefined
[10] IRCCS Ospedale San Raffaele,undefined
[11] Università Vita-Salute San Raffaele,undefined
来源：
Nature Cancer | 2023年 / 4卷
关键词：
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Real-world data (RWD) and real-world evidence (RWE) from heterogeneous data sources has the potential to transform oncology research, especially when coupled with artificial intelligence (AI). We discuss the issues involved in primary data capture and post-hoc AI analysis and propose using AI to support the capture of primary RWD.
引用
收藏
页码：1627 / 1629
页数：2
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