Artificial intelligence is rapidly becoming part of the conversation about women’s health, not as a futuristic add-on but as a practical tool with the potential to improve prevention, diagnosis, monitoring, and long-term care. Recent scientific literature shows that AI applications in obstetrics and gynaecology are expanding across multiple domains, from maternal health and gestational diabetes to gynaecological oncology, endometriosis, and mental health. At the same time, these advances are raising important questions about trust, inclusion, and fairness.
One of the clearest trends is the shift away from generalised care models towards more individualised approaches. In women’s health, this is particularly relevant, as risk profiles, symptoms, treatment responses, and care pathways often vary substantially across the life course. AI can help manage this complexity by integrating large, heterogeneous data sources (e.g. clinical records, laboratory measures, imaging, patient-reported outcomes, and even digital behaviour) to support more tailored decision-making. Reviews in obstetrics and gynaecology published in 2024 and 2025 describe AI as an emerging enabler of more individualised and potentially more effective care, particularly where early detection and risk stratification are essential.
Maternal health is one of the most promising areas. The literature highlights AI’s role in identifying pregnancies at higher risk of complications, enabling earlier intervention, and improving access to monitoring and care. This is particularly important in settings where specialist access is limited or where delays in detection can have serious consequences for both mother and child. Recent reviews describe AI-based tools as potentially useful for enhancing maternal care pathways through predictive analytics, remote monitoring, and more targeted clinical attention.
Another fast-growing area is gestational diabetes. Here, AI is being explored for screening, diagnosis, and management to identify high-risk pregnancies earlier and support more responsive care. Because gestational diabetes sits at the intersection of metabolic health, pregnancy outcomes, and future long-term risk for both mother and child, it represents a strong example of how AI can contribute not only to immediate clinical decision-making but also to preventive medicine. Recent systematic reviews suggest that AI models may improve prediction and support more precise risk assessment, although further validation is still needed before widespread implementation.
Gynaecological oncology is also emerging as a key field for AI applications. Reviews published in 2025 highlight promising applications in early detection, image interpretation, treatment planning, and patient monitoring for cervical, ovarian, and endometrial cancers. These developments reflect a broader shift in oncology towards data-driven personalisation, in which AI may help clinicians extract clinically relevant patterns from increasingly complex datasets. However, the literature also emphasises that these tools must be rigorously validated and carefully integrated into real-world clinical workflows.
Beyond high-profile applications such as cancer or maternal risk prediction, AI may also add value in conditions that have historically been under-recognised or under-diagnosed. Endometriosis is a good example. Recent reviews suggest that AI could support not only diagnostic innovation but also patient education and access to reliable information. This matters because women with endometriosis often face long diagnostic delays, fragmented care, and inconsistent communication. In this context, AI may have a role not only in clinical prediction but also in improving health literacy and patient support.
Women’s mental health is another important frontier. Recent studies have explored AI-based prediction of postpartum depression, showing that machine learning models may help identify women at higher risk by using biopsychosocial and clinical variables. While these tools are not a substitute for clinical judgement, they point towards a future in which AI may contribute to more proactive and preventive approaches in perinatal mental health.
Yet the literature also makes it clear that innovation alone is insufficient. A central concern is that AI systems may reproduce or amplify existing disparities if they are developed using unrepresentative data or evaluated without attention to demographic performance. This issue is particularly relevant in women’s health, where many conditions are already characterised by delayed recognition, undertreatment, or unequal access to care. A recent systematic review of demographic disparities in medical large language models highlights persistent concerns about bias and the need for improved evaluation and mitigation strategies. More broadly, current evidence suggests that the future of AI in women’s health will depend not only on technical performance but also on whether systems are trustworthy, inclusive, and responsive to real-world diversity.
Therefore, AI in women’s health should not be framed simply as a matter of faster tools or smarter algorithms. Its real promise lies in supporting more predictive, preventive, personalised, and participatory care, while remaining attentive to ethics, transparency, and equity. The current literature suggests that the field is moving in this direction, but also that substantial work remains to ensure that AI benefits all women, across different conditions, settings, and stages of life.
References.
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