Joshi Anagha
Computational Biology Unit, Department of Clinical Science, University of Bergen, Bergen, Norway.
Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, India.
Front Big Data. 2024 Oct 16;7:1436019. doi: 10.3389/fdata.2024.1436019. eCollection 2024.
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.
人工智能和机器学习是快速发展的领域，有潜力通过提高诊断准确性、个性化治疗方案以及建立疾病进展预测模型以实现预防保健，从而改变女性健康状况。本文讨论了机器学习可促进实现可及、可负担、个性化且基于证据的医疗保健的三类女性健康问题。从这个角度出发，首先阐述了大数据和机器学习应用在女性健康背景下的前景。尽管有这些前景，但由于诸多问题，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等，机器学习应用在临床护理中并未得到广泛应用。在医学领域，对女性的歧视由来已久。机器学习在数据中隐含着偏差。因此，尽管机器学习有潜力改善女性健康的某些方面，但它也可能强化性别偏见。如果在没有正确理解和纠正社会文化中的性别偏见做法及政策的情况下盲目集成先进的机器学习工具，就不太可能在健康领域实现性别平等。
