TY  - JOUR
AU  - Istasy, Paul
AU  - Lee, Wen Shen
AU  - Iansavichene, Alla
AU  - Upshur, Ross
AU  - Gyawali, Bishal
AU  - Burkell, Jacquelyn
AU  - Sadikovic, Bekim
AU  - Lazo-Langner, Alejandro
AU  - Chin-Yee, Benjamin
PY  - 2022
DA  - 2022/11/1
TI  - The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review
JO  - J Med Internet Res
SP  - e39748
VL  - 24
IS  - 11
KW  - artificial intelligence
KW  - eHealth
KW  - digital health
KW  - machine learning
KW  - oncology
KW  - cancer
KW  - health equity
KW  - health disparity
KW  - bias
KW  - global health
KW  - public health
KW  - cancer epidemiology
KW  - epidemiology
KW  - scoping
KW  - review
KW  - mobile phone
AB  - Background: The field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity. Objective: We aimed to conduct 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?” Methods: Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) 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 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology. Results: Of the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes. Conclusions: Our scoping review revealed 3 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 a lack of discussion of ethical challenges with the 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 a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles. 
SN  - 1438-8871
UR  - https://www.jmir.org/2022/11/e39748
UR  - https://doi.org/10.2196/39748
UR  - http://www.ncbi.nlm.nih.gov/pubmed/36005841
DO  - 10.2196/39748
ID  - info:doi/10.2196/39748
ER  -