Big data and AI for gender equality in health: bias is a big challenge.

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Frontiers in Big Data Pub Date : 2024-10-16 eCollection Date: 2024-01-01 DOI:10.3389/fdata.2024.1436019
Anagha Joshi
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Abstract

Artificial intelligence and machine learning are rapidly evolving fields that have the potential to transform women's health by improving diagnostic accuracy, personalizing treatment plans, and building predictive models of disease progression leading to preventive care. Three categories of women's health issues are discussed where machine learning can facilitate accessible, affordable, personalized, and evidence-based healthcare. In this perspective, firstly the promise of big data and machine learning applications in the context of women's health is elaborated. Despite these promises, machine learning applications are not widely adapted in clinical care due to many issues including ethical concerns, patient privacy, informed consent, algorithmic biases, data quality and availability, and education and training of health care professionals. In the medical field, discrimination against women has a long history. Machine learning implicitly carries biases in the data. Thus, despite the fact that machine learning has the potential to improve some aspects of women's health, it can also reinforce sex and gender biases. Advanced machine learning tools blindly integrated without properly understanding and correcting for socio-cultural sex and gender biased practices and policies is therefore unlikely to result in sex and gender equality in health.

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大数据和人工智能促进卫生领域的性别平等:偏见是一大挑战。
人工智能和机器学习是快速发展的领域,通过提高诊断准确性、个性化治疗方案和建立疾病进展预测模型以实现预防保健,它们有可能改变妇女的健康状况。本文讨论了三类妇女健康问题,在这些问题中,机器学习可以促进可获得、可负担、个性化和基于证据的医疗保健。在这一视角中,首先阐述了大数据和机器学习应用在妇女健康方面的前景。尽管有这些前景,但由于许多问题,包括伦理问题、患者隐私、知情同意、算法偏差、数据质量和可用性以及医疗保健专业人员的教育和培训,机器学习应用并未广泛应用于临床医疗。在医疗领域,对女性的歧视由来已久。机器学习隐含着数据中的偏见。因此,尽管机器学习有可能改善女性健康的某些方面,但它也可能强化性和性别偏见。因此,盲目整合先进的机器学习工具,而不正确理解和纠正社会文化中带有性和性别偏见的做法和政策,不太可能实现健康领域的性和性别平等。
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来源期刊
CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
期刊最新文献
Exploring code portability solutions for HEP with a particle tracking test code. Editorial: Utilizing big data and deep learning to improve healthcare intelligence and biomedical service delivery. Big data and AI for gender equality in health: bias is a big challenge. Integrating longitudinal mental health data into a staging database: harnessing DDI-lifecycle and OMOP vocabularies within the INSPIRE Network Datahub. AI security and cyber risk in IoT systems.
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