AI technologies outperform traditional methods for semen analysis in male infertilityCan AI help diagnose male infertility better than traditional methods?

Frontiers in Medicine Published April 3, 2026 DOI ↗ Editorial oversight: Dr. Lars van Dijk, PhD · Surgical, Procedural & Diagnostic

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Key Takeaway

Consider AI for semen analysis potential, but recognize need for validation and standardization.

This narrative review examined how artificial intelligence technologies are being applied to the diagnosis and treatment of male infertility. The review describes AI applications that automate and refine semen analysis, reporting that AI algorithms outperform traditional methods. However, no specific study designs, population details, sample sizes, or quantitative effect measures were reported in this descriptive synthesis.

The review did not report on safety, adverse events, or tolerability of AI technologies in this context. Key limitations identified include the need for further multicenter validation of AI-based models, methodological standardization across studies, and careful consideration of ethical and privacy issues related to AI implementation in healthcare.

Practice relevance is framed cautiously, suggesting AI could enable more accessible and personalized diagnoses and open new perspectives for individualized treatments. However, the evidence presented is associative and descriptive, based on a review of existing applications rather than new data synthesis or meta-analysis. The authors explicitly caution against overstating findings to claim AI improves fertilization and pregnancy outcomes or transforms diagnosis and treatment.

When a couple struggles to conceive, figuring out why can be a long and uncertain process. A new review of existing research suggests artificial intelligence might help with one piece of that puzzle: analyzing sperm samples. The review found that AI algorithms are being developed to automate semen analysis and that, in these studies, they appear to outperform traditional manual methods. This points toward a future where diagnosis could be faster and more consistent. However, this is a narrative review — it describes what other studies have found but doesn't combine their data in a rigorous new analysis. The authors themselves are cautious. They note that the AI models need to be tested across multiple medical centers to prove they work for everyone. They also highlight that standardizing these methods and carefully addressing patient privacy and ethical questions are essential next steps before this technology becomes routine in clinics.

What this means for you:

AI shows promise for semen analysis, but more validation and ethical safeguards are needed.

Study Details

Study typeSystematic review

EvidenceLevel 1

PublishedApr 2026

View Original 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.

AI technologies outperform traditional methods for semen analysis in male infertilityCan AI help diagnose male infertility better than traditional methods?

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AI technologies outperform traditional methods for semen analysis in male infertilityCan AI help diagnose male infertility better than traditional methods?

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