Harnessing oncology real-world data with AI

Piers Mahon; Geoff Hall; Andre Dekker; Janne Vehreschild; Giovanni Tonon

doi:10.1038/s43018-023-00689-7

Harnessing oncology real-world data with AI

Piers Mahon, Geoff Hall, Andre Dekker, Janne Vehreschild, Giovanni Tonon^*

^*Corresponding author for this work

Research output: Contribution to journal › Editorial › Academic › peer-review

Abstract

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.

Original language	English
Pages (from-to)	1627–1629
Number of pages	3
Journal	Nature Cancer
Volume	4
Issue number	12
Early online date	1 Dec 2023
DOIs	https://doi.org/10.1038/s43018-023-00689-7
Publication status	Published - 1 Dec 2023

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10.1038/s43018-023-00689-7

Cite this

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title = "Harnessing oncology real-world data with AI",

abstract = "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.",

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AU - Dekker, Andre

AU - Vehreschild, Janne

AU - Tonon, Giovanni

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AB - 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.

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DO - 10.1038/s43018-023-00689-7

M3 - Editorial

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JO - Nature Cancer

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