A guide to artificial intelligence for cancer researchers

Raquel Perez-Lopez¹, Narmin Ghaffari Laleh², Faisal Mahmood^{3

4

5

6

7

8}, Jakob Nikolas Kather^{9

10

11}

Affiliations

¹ Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.
² Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany.
³ Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁴ Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
⁵ Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
⁶ Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
⁷ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
⁸ Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA.
⁹ Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany. jakob_nikolas.kather@tu-dresden.de.
¹⁰ Department of Medicine I, University Hospital Dresden, Dresden, Germany. jakob_nikolas.kather@tu-dresden.de.
¹¹ Medical Oncology, National Center for Tumour Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. jakob_nikolas.kather@tu-dresden.de.

PMID: 38755439
DOI: 10.1038/s41568-024-00694-7

Review

A guide to artificial intelligence for cancer researchers

Raquel Perez-Lopez et al. Nat Rev Cancer. 2024 Jun.

. 2024 Jun;24(6):427-441.

doi: 10.1038/s41568-024-00694-7. Epub 2024 May 16.

Authors

Raquel Perez-Lopez¹, Narmin Ghaffari Laleh², Faisal Mahmood^{3

4

5

6

7

8}, Jakob Nikolas Kather^{9

10

11}

Affiliations

¹ Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.
² Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany.
³ Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁴ Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
⁵ Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
⁶ Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
⁷ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
⁸ Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA.
⁹ Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany. jakob_nikolas.kather@tu-dresden.de.
¹⁰ Department of Medicine I, University Hospital Dresden, Dresden, Germany. jakob_nikolas.kather@tu-dresden.de.
¹¹ Medical Oncology, National Center for Tumour Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. jakob_nikolas.kather@tu-dresden.de.

PMID: 38755439
DOI: 10.1038/s41568-024-00694-7

Abstract

Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to a readily accessible tool for cancer researchers. AI-based tools can boost research productivity in daily workflows, but can also extract hidden information from existing data, thereby enabling new scientific discoveries. Building a basic literacy in these tools is useful for every cancer researcher. Researchers with a traditional biological science focus can use AI-based tools through off-the-shelf software, whereas those who are more computationally inclined can develop their own AI-based software pipelines. In this article, we provide a practical guide for non-computational cancer researchers to understand how AI-based tools can benefit them. We convey general principles of AI for applications in image analysis, natural language processing and drug discovery. In addition, we give examples of how non-computational researchers can get started on the journey to productively use AI in their own work.

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References

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A guide to artificial intelligence for cancer researchers

Affiliations

A guide to artificial intelligence for cancer researchers

Authors

Affiliations

Abstract

References

Publication types

MeSH terms

LinkOut - more resources

Full Text Sources

Medical