Artificial Intelligence and Cancer Control: Toward Prioritizing Justice, Equity, Diversity, and Inclusion (JEDI) in Emerging Decision Support Technologies.

Autor: Taber P; Department of Biomedical Informatics, University of Utah School of Medicine, 421 Wakara Way, Salt Lake City, UT, 84108, USA. peter.taber@hsc.utah.edu., Armin JS; Department of Family and Community Medicine, University of Arizona College of Medicine, Tucson, AZ, USA., Orozco G; University of Arizona College of Medicine, Tucson, AZ, USA., Del Fiol G; Department of Biomedical Informatics, University of Utah School of Medicine, 421 Wakara Way, Salt Lake City, UT, 84108, USA., Erdrich J; Division of Surgical Oncology, University of Arizona College of Medicine, Tucson, AZ, USA., Kawamoto K; Department of Biomedical Informatics, University of Utah School of Medicine, 421 Wakara Way, Salt Lake City, UT, 84108, USA., Israni ST; Presence Center, Stanford University School of Medicine, Palo Alto, CA, USA.
Jazyk: angličtina
Zdroj: Current oncology reports [Curr Oncol Rep] 2023 May; Vol. 25 (5), pp. 387-424. Date of Electronic Publication: 2023 Feb 22.
DOI: 10.1007/s11912-023-01376-7
Abstrakt: 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. 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.
(© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
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