TY - JOUR
T1 - Artificial Intelligence and Cancer Control
T2 - Toward Prioritizing Justice, Equity, Diversity, and Inclusion (JEDI) in Emerging Decision Support Technologies
AU - Taber, Peter
AU - Armin, Julie S.
AU - Orozco, Gabriela
AU - Del Fiol, Guilherme
AU - Erdrich, Jennifer
AU - Kawamoto, Kensaku
AU - Israni, Sonoo Thadaney
N1 - Publisher Copyright: © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2023/5
Y1 - 2023/5
N2 - Purpose for Review: This perspective piece has two goals: first, to describe issues related to artificial intelligence-based applications for cancer control as they may impact health inequities or disparities; and second, to report on a review of systematic reviews and meta-analyses of artificial intelligence-based tools for cancer control to ascertain the extent to which discussions of justice, equity, diversity, inclusion, or health disparities manifest in syntheses of the field’s best evidence. Recent Findings: We found that, while a significant proportion of existing syntheses of research on AI-based tools in cancer control use formal bias assessment tools, the fairness or equitability of models is not yet systematically analyzable across studies. Issues related to real-world use of AI-based tools for cancer control, such as workflow considerations, measures of usability and acceptance, or tool architecture, are more visible in the literature, but still addressed only in a minority of reviews. Summary: Artificial intelligence is poised to bring significant benefits to a wide range of applications in cancer control, but more thorough and standardized evaluations and reporting of model fairness are required to build the evidence base for AI-based tool design for cancer and to ensure that these emerging technologies promote equitable healthcare.
AB - Purpose for Review: This perspective piece has two goals: first, to describe issues related to artificial intelligence-based applications for cancer control as they may impact health inequities or disparities; and second, to report on a review of systematic reviews and meta-analyses of artificial intelligence-based tools for cancer control to ascertain the extent to which discussions of justice, equity, diversity, inclusion, or health disparities manifest in syntheses of the field’s best evidence. Recent Findings: We found that, while a significant proportion of existing syntheses of research on AI-based tools in cancer control use formal bias assessment tools, the fairness or equitability of models is not yet systematically analyzable across studies. Issues related to real-world use of AI-based tools for cancer control, such as workflow considerations, measures of usability and acceptance, or tool architecture, are more visible in the literature, but still addressed only in a minority of reviews. Summary: Artificial intelligence is poised to bring significant benefits to a wide range of applications in cancer control, but more thorough and standardized evaluations and reporting of model fairness are required to build the evidence base for AI-based tool design for cancer and to ensure that these emerging technologies promote equitable healthcare.
KW - Artificial intelligence
KW - Cancer
KW - Diversity
KW - Equity
KW - Health disparities
KW - Justice
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U2 - 10.1007/s11912-023-01376-7
DO - 10.1007/s11912-023-01376-7
M3 - Review article
C2 - 36811808
SN - 1523-3790
VL - 25
SP - 387
EP - 424
JO - Current oncology reports
JF - Current oncology reports
IS - 5
ER -