A Systematic Review on Machine Learning (ML) and Artificial Intelligence (AI) in Understanding and Assessing Women's Health

While depression remains a prominent health concern with its associated difficulties, recent years have witnessed the utilization of artificial intelligence (AI) and machine learning (ML) to analyze extensive datasets, thereby enhancing the detection and diagnosis of women's health risks. We conducted a systematic review of AI and ML applications in the context of women's health. This review was carried out using various databases, including PubMed, Scopus, ACM Digital Library, Web-of-Science, and IEEE Explore Digital. We employed specific search terms related to mental health and ML, supplementing our database searches with manual exploration. Our inclusion criteria encompassed articles in English published between 2010 and 2022, focusing on machine learning and artificial intelligence applications and algorithms in the domain of women health, particularly in relation to depression among women. Initially, we identified a total of 495 records based on abstract searches. After removing duplicates, 425 unique reports remained, of which 380 were subsequently excluded following abstract screening. We then assessed 45 full-text articles for eligibility, revealing a wide range of methodologies and outcomes. Notably, the results indicated a high level of accuracy in risk classification, exceeding 90%. Our findings provide valuable perspectives on the implementation and application of AI and ML within the domain of women's health. These insights underscore the potential of these technologies to propel advancements in interventions related to women's health. In summary, we present initial insights and propose future pathways for harnessing AI and ML technologies to tackle depression challenges to enhance women's health on a global scale.