ReviewFrontiers in urology2026
Breaking barriers in male infertility: the power of artificial intelligence-driven solutions.
Review in Frontiers in urology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Evaluation of vision transformers and vision foundation models for sperm morphology analysis.Frontiers in reproductive health · 2026Article
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
Infertility is defined as the inability of a sexually active couple, not using contraception, to achieve a spontaneous pregnancy within 12 months. It affects an estimated 8% to 12% of couples worldwide, with 30% to 50% of cases attributable, either primarily or in part, to male factors. Despite the increasing number of assisted reproductive technology (ART) procedures performed globally, improvements in fertilization and pregnancy outcomes have been limited. The need to improve diagnostic accuracy and therapeutic efficiency has driven the development of artificial intelligence (AI) in reproductive medicine. This narrative review aims to explore how AI is transforming the diagnosis and treatment of male infertility. AI technologies are nowadays being used to automate and refine semen analysis, providing more reliable assessments of sperm morphology, motility, and concentration. These innovations enable clinicians to improve the prediction of semen quality and to identify which patients might benefit most from specific interventions, such as sperm retrieval in cases of non-obstructive azoospermia or the selection of optimal sperm cells for reproductive techniques. Moreover, advanced AI algorithms-including support vector machines, deep neural networks, and decision trees-outperform traditional methods, offering greater precision and reducing subjectivity in laboratory evaluations. Additionally, AI is being utilized to estimate the chances of success with assisted reproductive techniques, assess sperm DNA fragmentation, and guide the selection of sperm. The integration of AI into clinical practice not only enables more accessible and personalized diagnoses but also opens new perspectives for the development of individualized treatments, optimizing reproductive outcomes. However, further multicenter validation of AI-based models, methodological standardization, and careful consideration of ethical and privacy issues are necessary before widespread clinical adoption.
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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.