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利用人工智能驱动技术加速联合国可持续发展目标:关于妇女医疗保健的系统文献综述

Accelerating UN Sustainable Development Goals with AI-Driven Technologies: A Systematic Literature Review of Women's Healthcare.

作者信息

Lau Pin Lean, Nandy Monomita, Chakraborty Sushmita

机构信息

Brunel Law School, Brunel University London, Uxbridge UB8 3PH, UK.

Brunel Business School, Brunel University London, Uxbridge UB8 3PH, UK.

出版信息

Healthcare (Basel). 2023 Jan 31;11(3):401. doi: 10.3390/healthcare11030401.

Abstract

In 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.

摘要

在本文中,我们批判性地审视了人工智能(AI)在医疗保健领域的贡献是否充分涵盖了女性医疗保健领域。这对于实现和加速联合国确定的性别平等与健康可持续发展目标(SDGs)具有重要意义。通过系统的文献综述(SLR),我们研究了在更广泛的医疗保健提供体系中,健康与生物医学领域的人工智能应用是否充分体现了女性健康。我们的研究结果根据女性健康的主题标志分为不同类别,这些标志与以下假设相符:人工智能驱动的技术在女性健康领域的应用仍未得到充分体现,但强调其未来的应用可以提高明智健康选择的效率,并且对于小型或代表性不足社区的女性来说尤其容易获得。同时,这些研究结果可以协助并影响政府政策的制定、可及性以及监管环境,以实现可持续发展目标。在更大范围内,在不久的将来,我们将扩展关于人工智能驱动技术在健康可持续发展目标应用方面的现有文献,并为未来的研究设定议程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c57c/9914215/8ff4347d83db/healthcare-11-00401-g001.jpg

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