Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: May 21, 2022
Open Peer Review Period: May 20, 2022 - Jul 15, 2022
Date Accepted: Aug 24, 2022
Date Submitted to PubMed: Aug 25, 2022
(closed for review but you can still tweet)
The impact of artificial intelligence on health equity in oncology: A scoping review
ABSTRACT
Background:
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.
Objective:
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?
Methods:
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.
Results:
133 records of the 14011 identified from our review were included. We identified three general themes in the literature: the use of AI to reduce healthcare disparities (n=58), concerns surrounding AI technologies and bias (n=15), and the use of AI to examine biological and social determinants of health (n=55). Five articles touched on multiple of these themes.
Conclusions:
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. Further research should focus on addressing these gaps to ensure more equitable integration of AI in cancer research and clinical practice.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.