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Harnessing oncology real-world data with AI

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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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Authors and Affiliations

  1. Digital Institute for Cancer Outcomes Research E.E.I.G, Brussels, Belgium

    Piers Mahon

  2. IQVIA Cancer Research BV, Brussels, Belgium

    Piers Mahon

  3. Leeds Teaching Hospital NHS Trust, Leeds, UK

    Geoff Hall

  4. Health Data Research, London, UK

    Geoff Hall

  5. Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands

    Andre Dekker

  6. University Hospital of Frankfurt, Center for Internal Medicine, Department II — Hematology/Oncology, Frankfurt, Germany

    Janne Vehreschild

  7. Center for Omics Sciences, IRCCS Ospedale San Raffaele, Milan, Italy

    Giovanni Tonon

  8. Università Vita-Salute San Raffaele, Milan, Italy

    Giovanni Tonon

Authors
  1. Piers Mahon
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  2. Geoff Hall
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  3. Andre Dekker
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  4. Janne Vehreschild
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  5. Giovanni Tonon
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Corresponding author

Correspondence to Giovanni Tonon.

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Mahon, P., Hall, G., Dekker, A. et al. Harnessing oncology real-world data with AI. Nat Cancer 4, 1627–1629 (2023). https://doi.org/10.1038/s43018-023-00689-7

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  • DOI: https://doi.org/10.1038/s43018-023-00689-7

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