The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations

Abstract

There is clear evidence to suggest that diabetes does not affect all populations equally. Among adults living with diabetes, those from ethnoracial minority communities—foreign-born, immigrant, refugee, and culturally marginalized—are at increased risk of poor health outcomes. Artificial intelligence (AI) is actively being researched as a means of improving diabetes management and care; however, several factors may predispose AI to ethnoracial bias. To better understand whether diabetes AI interventions are being designed in an ethnoracially equitable manner, we conducted a secondary analysis of 141 articles included in a 2018 review by Contreras and Vehi entitled “Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.” Two members of our research team independently reviewed each article and selected those reporting ethnoracial data for further analysis. Only 10 articles (7.1%) were ultimately selected for secondary analysis in our case study. Of the 131 excluded articles, 118 (90.1%) failed to mention participants’ ethnic or racial backgrounds. The included articles reported ethnoracial data under various categories, including race (n=6), ethnicity (n=2), race/ethnicity (n=3), and percentage of Caucasian participants (n=1). Among articles specifically reporting race, the average distribution was 69.5% White, 17.1% Black, and 3.7% Asian. Only 2 articles reported inclusion of Native American participants. Given the clear ethnic and racial differences in diabetes biomarkers, prevalence, and outcomes, the inclusion of ethnoracial training data is likely to improve the accuracy of predictive models. Such considerations are imperative in AI-based tools, which are predisposed to negative biases due to their black-box nature and proneness to distributional shift. Based on our findings, we propose a short questionnaire to assess ethnoracial equity in research describing AI-based diabetes interventions. At this unprecedented time in history, AI can either mitigate or exacerbate disparities in health care. Future accounts of the infancy of diabetes AI must reflect our early and decisive action to confront ethnoracial inequities before they are coded into our systems and perpetuate the very biases we aim to eliminate. If we take deliberate and meaningful steps now toward training our algorithms to be ethnoracially inclusive, we can architect innovations in diabetes care that are bound by the diverse fabric of our society.

Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal

“…According to our results, most of the AI research in CBPHC has taken place in North American and Europe-centric settings. Several factors contribute to ethnoracial biases when using AI, including not accounting for ethnoracial information, thereby ignoring the different effects illnesses can have on different populations [ 113 ]. Consequently, studies can yield results with historical biases as well as biases related to over- or under-representation of population characteristics in data sets and in the knowledge, bases used to build AI systems.…”

Consumer Health Informatics for Racial and Ethnic Minoritized Communities: Minor Progress, Major Opportunities

“…A total of twenty articles were included in the review [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. A significant portion of the included articles were original research (n=18) [13,[15][16][17][18][19][20][21][22][23][24][25][26][28][29][30][31][32].…”

“…A significant portion of the included articles were original research (n=18) [13,[15][16][17][18][19][20][21][22][23][24][25][26][28][29][30][31][32]. One perspective piece and one review were also included [14,27]. The publication venues were mostly in journals focused on medicine and public health [13-17, 19, 20, 22-25, 28, 30, 32], with only four of the included articles published in traditional informatics journals [18,21,27,29] and two published in the proceed-ings of computing-related conferences [26,31].…”

Artificial Intelligence for the Diagnosis and Treatment of Diabetes Kidney Disease: a systematic review

“…The field of DL has advanced massively in the ability of machines to understand and data including the likes of images, language and speech. In contrast to ML, DL is a form of representation learning composed of numerous layers -gaining the ability to learn highly complex functions (33). DL algorithms such as Convolutional Neural Network (CNN) is designed to process data that exhibits natural spatial invariances.…”