Please use this identifier to cite or link to this item:
Title: Accelerating UN Sustainable Development Goals with AI-Driven Technologies: A Systematic Literature Review of Women’s Healthcare
Authors: Lau, PL
Nandy, M
Chakraborty, S
Keywords: women’s healthcare;artificial Intelligence;UN sustainable development goals;SDG3;SDG5;gender equality;health equality;health sustainability
Issue Date: 31-Jan-2023
Publisher: MDPI AG
Citation: Lau, P.L., Nandy, M. and Chakraborty, S. (2023) 'Accelerating UN Sustainable Development Goals with AI-Driven Technologies: A Systematic Literature Review of Women’s Healthcare', Healthcare, 11 (3), pp. 1 - 16. doi: 10.3390/healthcare11030401.
Abstract: Copyright © 2023 by the authors. n this paper, we critically examine if the contributions of artificial intelligence (AI) in healthcare adequately represent the realm of women’s healthcare. This would be relevant for achieving and accelerating the gender equality and health sustainability goals (SDGs) defined by the United Nations. Following a systematic literature review (SLR), we examine if AI applications in health and biomedicine adequately represent women’s health in the larger scheme of healthcare provision. Our findings are divided into clusters based on thematic markers for women’s health that are commensurate with the hypotheses that AI-driven technologies in women’s health still remain underrepresented, but that emphasis on its future deployment can increase efficiency in informed health choices and be particularly accessible to women in small or underrepresented communities. Contemporaneously, these findings can assist and influence the shape of governmental policies, accessibility, and the regulatory environment in achieving the SDGs. On a larger scale, in the near future, we will extend the extant literature on applications of AI-driven technologies in health SDGs and set the agenda for future research.
Description: Data Availability Statement This study is primarily a reanalysis of existing publicly available data as cited in the “References” section. Notwithstanding, in some sections of this publication, the data underpinning parts thereof can be accessed from Brunel University London’s data repository, Brunelfigshare here under a CCBY license: publication (accessed on 21 July 2022), where it is supported by multiple datasets cited in the “References” section of this paper.
Appears in Collections:Brunel OA Publishing Fund
Brunel Law School Research Papers
Brunel Business School Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdfCopyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons