The impact of artificial intelligence on health equity in oncology: A scoping review


The field of oncology is at the forefront of advances in artificial intelligence (AI) in healthcare, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize healthcare delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity.


We conducted a scoping review of the literature to address the question: What are the current and potential impacts of AI technologies on health equity in oncology?


Following PRISMA-ScR guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the three key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology.


133 records of the 14011 identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce healthcare disparities (n=58/133; 43.5%), concerns surrounding AI technologies and bias (n=16/133; 12.0%), and the use of AI to examine biological and social determinants of health (n=55/133; 41.4%). Four articles (3.0%) touched on multiple of these themes.


Our scoping review revealed three main themes on the impact of AI on health equity in oncology, which relate to AI’s ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included lack of discussion of ethical challenges with application of AI technologies in Low- and Middle-Income Countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure more equitable integration of AI in cancer research and clinical practice. Limitations of our study includes its exploratory nature, its focus on oncology as opposed to all healthcare sectors, as well as its analysis of solely English-language articles.

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