Abstract
Artificial intelligence (AI) stands at the threshold of revolutionizing clinical oncology, with considerable potential to improve early cancer detection and risk assessment, and to enable more accurate personalized treatment recommendations. However, a notable imbalance exists in the distribution of the benefits of AI, which disproportionately favour those living in specific geographical locations and in specific populations. In this Perspective, we discuss the need to foster the development of equitable AI tools that are both accurate in and accessible to a diverse range of patient populations, including those in low-income to middle-income countries. We also discuss some of the challenges and potential solutions in attaining equitable AI, including addressing the historically limited representation of diverse populations in existing clinical datasets and the use of inadequate clinical validation methods. Additionally, we focus on extant sources of inequity including the type of model approach (such as deep learning, and feature engineering-based methods), the implications of dataset curation strategies, the need for rigorous validation across a variety of populations and settings, and the risk of introducing contextual bias that comes with developing tools predominantly in high-income countries.
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V.S.V. and A.M. researched data, made a substantial contribution to discussions of content and wrote the manuscript. All authors reviewed and/or edited the manuscript prior to submission.
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A.M. has acted as an adviser and/or consultant to the Frederick National Laboratory, Picture Health, SimBioSys and Takeda, has received research funding from AstraZeneca and Bristol Myers-Squibb, is listed on two R01 grants alongside Inspirata Inc., and is listed on patents licensed to Elucid Bioimaging and Picture Health. The other authors declare no competing interests.
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Viswanathan, V.S., Parmar, V. & Madabhushi, A. Towards equitable AI in oncology. Nat Rev Clin Oncol (2024). https://doi.org/10.1038/s41571-024-00909-8
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DOI: https://doi.org/10.1038/s41571-024-00909-8
