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Translation of AI into oncology clinical practice

Oncogene volume 42, pages 3089–3097 (2023)Cite this article

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Abstract

Artificial intelligence (AI) is a transformative technology that is capturing popular imagination and can revolutionize biomedicine. AI and machine learning (ML) algorithms have the potential to break through existing barriers in oncology research and practice such as automating workflow processes, personalizing care, and reducing healthcare disparities. Emerging applications of AI/ML in the literature include screening and early detection of cancer, disease diagnosis, response prediction, prognosis, and accelerated drug discovery. Despite this excitement, only few AI/ML models have been properly validated and fewer have become regulated products for routine clinical use. In this review, we highlight the main challenges impeding AI/ML clinical translation. We present different clinical use cases from the domains of radiology, radiation oncology, immunotherapy, and drug discovery in oncology. We dissect the unique challenges and opportunities associated with each of these cases. Finally, we summarize the general requirements for successful AI/ML implementation in the clinic, highlighting specific examples and points of emphasis including the importance of multidisciplinary collaboration of stakeholders, role of domain experts in AI augmentation, transparency of AI/ML models, and the establishment of a comprehensive quality assurance program to mitigate risks of training bias and data drifts, all culminating toward safer and more beneficial AI/ML applications in oncology labs and clinics.

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Fig. 1: Example of the role of AI in the radiation therapy workflow [3].
Fig. 2: Factors addressing responsibility diffusion in AI-driven clinical decision-making.

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Acknowledgements

This work was partly supported by National Institute of Health (NIH) grant R01-CA233487 and its supplement (CA233487-05S1). IEN and KP would like to acknowledge support from the Center for Advanced Studies at Ludwig-Maximilians-Universität München (CAS LMU fellowship).

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Authors and Affiliations

  1. Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA

    Issam El Naqa, Aleksandra Karolak & Yi Luo

  2. Diagnostic Imaging & Interventional Radiology, Moffitt Cancer Center, Tampa, FL, 33612, USA

    Les Folio

  3. Cutaneous Oncology and Immunology, Moffitt Cancer Center, Tampa, FL, 33612, USA

    Ahmad A. Tarhini

  4. Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, 33612, USA

    Dana Rollison

  5. Department of Medical Physics, Ludwig-Maximilians-Universität München, Munich, Germany

    Katia Parodi

Authors
  1. Issam El Naqa
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  2. Aleksandra Karolak
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  3. Yi Luo
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  4. Les Folio
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  5. Ahmad A. Tarhini
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  6. Dana Rollison
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  7. Katia Parodi
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Contributions

IEN conceived the study and drafted the outline. All authors contributed to the writeup of the manuscript.

Corresponding author

Correspondence to Issam El Naqa.

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Competing interests

IEN is on the scientific advisory of Endectra, LLC., act as deputy editor for the journal of Medical Physics and receives funding from NIH and DoD. DER is on the Board of Directors for NanoString technologies, Inc. LRF Research agreement: Philips Healthcare (ended Aug 2021). Patents (no royalties since NIH and military owned). Author royalties, Springer. AAT reports contracted research grants with institution from Bristol Myers Squib, Genentech-Roche, Regeneron, Sanofi-Genzyme, Nektar, Clinigen, Merck, Acrotech, Pfizer, Checkmate, OncoSec; personal consultant/advisory board fees from Bristol Myers Squibb, Merck, Easai, Instil Bio Clinigin, Regeneron, Sanofi-Genzyme, Novartis, Partner Therapeutics, Genentech/Roche, BioNTech, Concert AI, AstraZeneca outside the submitted work.

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El Naqa, I., Karolak, A., Luo, Y. et al. Translation of AI into oncology clinical practice. Oncogene 42, 3089–3097 (2023). https://doi.org/10.1038/s41388-023-02826-z

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