ReviewQatar medical journal2026
The Impact of Artificial Intelligence on Women's Healthcare: A Systematic Review.
Review in Qatar medical journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: 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. Methods: 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. Results: 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. Conclusion: 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.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.