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Limiting bias in AI models for improved and equitable cancer care

Cancer screening, diagnosis and care stand to benefit greatly from advances in artificial intelligence (AI). Researchers, developers and deployers must ensure that applications of AI avoid known racial and gender biases to advance health care for all.

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References

  1. Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. AI in health and medicine. Nat. Med. 28, 31–38 (2022).

    Article  CAS  PubMed  Google Scholar 

  2. Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y. & Ghassemi, M. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27, 2176–2182 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Lazar, S. & Nelson, A. AI safety on whose terms? Science 381, 138 (2023).

    Article  PubMed  Google Scholar 

  4. Amboree, T. L. et al. National breast, cervical, and colorectal cancer screening use in federally qualified health centers. JAMA Intern. Med. 184, 671–679 (2024).

    Article  PubMed  Google Scholar 

  5. Spencer, J. C. & Pignone, M. P. Cancer screening through federally qualified health centers. JAMA Intern. Med. 184, 679–680 (2024).

    Article  PubMed  Google Scholar 

  6. Khorana, A. A., Kuderer, N. M., Culakova, E., Lyman, G. H. & Francis, C. W. Development and validation of a predictive model for chemotherapy-associated thrombosis. Blood 111, 4902–4907 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Khorana, A. A. et al. Rivaroxaban for thromboprophylaxis in high-risk ambulatory patients with cancer. N. Engl. J. Med. 380, 720–728 (2019).

    Article  CAS  PubMed  Google Scholar 

  8. Ek, L. et al. Randomized phase III trial of low-molecular-weight heparin enoxaparin in addition to standard treatment in small-cell lung cancer: the RASTEN trial. Ann. Oncol. 29, 398–404 (2018).

    Article  CAS  PubMed  Google Scholar 

  9. Gichoya, J. W. et al. AI recognition of patient race in medical imaging: a modelling study. Lancet Digit. Health 4, e406–e414 (2022).

    CAS  Google Scholar 

  10. Adam, H. et al. Write it like you see it: Detectable differences in clinical notes by race lead to differential model recommendations. In Proc. 2022 AAAI/ACM Conference on AI, Ethics, and Society 7–21 (ACM, 2022).

  11. Xiao, Y., Lim, S., Pollard, T. J. & Ghassemi, M. In the name of fairness: assessing the bias in clinical record de-identification. In Proc. 2023 ACM Conference on Fairness, Accountability, and Transparency 123–137 (ACM, 2023).

  12. Ebrahimian, S. et al. FDA-regulated AI algorithms: trends, strengths, and gaps of validation studies. Acad. Radiol. 29, 559–566 (2022).

    Article  PubMed  Google Scholar 

  13. Yang, Y., Zhang, H., Katabi, D. & Ghassemi, M. Change is hard: a closer look at subpopulation shift. In Proc. 40th International Conference on Machine Learning 39584–39622 (JMLR, 2023).

  14. Bondi-Kelly, E. et al. Taking off with AI: lessons from aviation for healthcare. In Proc. 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization 1–14 (ACM, 2023).

  15. Marcus, L. et al. FDA approval summary: pembrolizumab for the treatment of tumor mutational burden-high solid tumors. Clin. Cancer Res. 27, 4685–4689 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Correspondence to Marzyeh Ghassemi.

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Ghassemi, M., Gusev, A. Limiting bias in AI models for improved and equitable cancer care. Nat Rev Cancer (2024). https://doi.org/10.1038/s41568-024-00739-x

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