The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review

Abstract

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.

Uses and limitations of artificial intelligence for oncology

“…Use cases for AI in medicine and epidemiology that we did not cover here but are equally important to deliberate on include the application of AI to clinical trial enrollment and for the study of disease development and progression, cancer genomics and genetic mutations, digital health and mobile monitoring of disease status, and population-level risk factors. [34][35][36][37] AI algorithms are only as good as the data and assumptions Beyond biases baked into the training data, human biases can affect how AI algorithms are used in the clinic. In a 2019 survey of physicians in Korea, 83.4% appreciated the usefulness of AI in medicine especially for medical diagnosis, but only 5.9% were familiar with AI and 29.3% acknowledged that AI cannot help in unexpected situations because of inadequate information.…”

“…This review only touched the surface of the potential for AI systems in oncology. Use cases for AI in medicine and epidemiology that we did not cover here but are equally important to deliberate on include the application of AI to clinical trial enrollment and for the study of disease development and progression, cancer genomics and genetic mutations, digital health and mobile monitoring of disease status, and population‐level risk factors 34–37 …”

The Evolution and Reliability of Machine Learning Techniques for Oncology

“…As a result, these methods have been applied with the purpose of modelling the development of malignant disorders as well as the therapy of those conditions. In addition, the capacity of machine learning techniques to extract significant features from complicated datasets demonstrates the significance of these features [17][18][19].…”

Research and application of artificial intelligence in dentistry from lower-middle income countries – a scoping review

“…Therefore, the training datasets in dentistry generated from high-income countries would not accurately represent the population, features and disease patterns for LMICs. AI trained on datasets from high-income countries may thus introduce errors if applied in a differently placed population groups [ 8 ]. Failure to tune and align the model to a particular population could give rise to certain unintended consequences such as affecting fairness, introducing biases, and disrupting the appropriateness of that algorithm [ 9 ].…”

“…Failure to tune and align the model to a particular population could give rise to certain unintended consequences such as affecting fairness, introducing biases, and disrupting the appropriateness of that algorithm [ 9 ]. Therefore, it is quintessential that AI algorithms be trained upon context-specific environment, establishing their relevance and application [ 8 ].…”