The Impact of Artificial Intelligence on Women's Healthcare: A Systematic Review

Artificial intelligence (AI) is rapidly transforming healthcare delivery with substantial implications for women's health. This systematic review synthesizes current evidence on AI applications in women's healthcare, evaluates their contributions and limitations, and identifies key challenges for clinical implementation.

A systematic analysis of peer-reviewed literature was conducted through database searches, including PubMed, Scopus, Web of Science and the Cochrane Library, focusing on AI applications in the Obstetrics and Gynecology domains.

The analysis reveals extensive AI development across obstetrics and gynecology subfields, particularly in obstetric imaging, fetal monitoring, gynecologic oncology, and predictive models for delivery outcomes. Both machine learning (59% of studies) and knowledge-based systems (38% of studies) are represented. Most publications (82%) represent preliminary work such as proof-of-concept algorithms or methods, with clinical validation remaining largely unreported. Key implementation challenges include limited external validation, ethical concerns, and the need for specialized clinician competencies.

AI demonstrates significant potential to enhance diagnostic precision, personalized treatment, and support clinical decision-making in women's health. However, most applications remain investigational with substantial barriers to clinical translation. Future work should prioritize robust validation, standardized reporting, and interdisciplinary collaboration to realize AI's potential in women's healthcare.