ReviewInternational journal of reproductive biomedicine2026
Artificial intelligence in infertility treatment: Applications, challenges, and future directions: A narrative review.
Review in International journal of reproductive biomedicine, 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
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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.
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The application of artificial intelligence (AI) is expanding in all fields of medicine, including infertility treatment. This article examines the different uses of AI in reproductive healthcare. This article focuses on predictive models, imaging processing, and personalized therapeutic strategies. AI-based software is increasingly being deployed for diagnosis and patient prognosis, utilizing big data derived from patient information and clinical outcomes. Thus, the prediction reliability of embryo implantation in in-vitro fertilization procedures notably increases. Furthermore, AI is revolutionizing embryo selection and sperm quality assessment through computer-based image processing systems and intracytoplasmic morphologically selected sperm injection techniques, thereby improving accuracy and consistency compared to traditional embryologist-dependent methods that heavily rely on the embryologists' skills. The paper reflects AI as the main factor in the formation of patient-specific treatment plans, risk reduction, and increased clinical success rates. Notwithstanding the immense potential, the implementation of AI in infertility treatment faces tangible issues, among which issues around ethics, privacy, quality, and diversity of data are prominent. This paper reviews current studies on the state-of-the-art possible and real failures of AI-focused strategies, as well as future research directions in the treatment of infertility.
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What Socratic 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 Socratic graph.