Evidence map›Paper›PMID 39992554›Full record

ReviewCurrent urology reports2025

Artificial Intelligence in Andrology: A New Frontier in Male Infertility Diagnosis and Treatment.

Joseph Y Nashed, Kiera Liblik, Ali Dergham, Luke Witherspoon, Ryan Flannigan

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current urology reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Joseph Y NashedSchool of Medicine, Kingston Health Sciences Centre, Queen's University, Kingston, ON, Canada.
Kiera LiblikDepartment of Urology, Kingston Health Sciences Centre, Queen's University, Kingston, ON, Canada.
Ali DerghamDivision of Urology, Department of Surgery, The Ottawa Hospital and University of Ottawa, Ottawa, ON, Canada.
Luke WitherspoonDivision of Urology, Department of Surgery, The Ottawa Hospital and University of Ottawa, Ottawa, ON, Canada.
Ryan FlanniganDepartment of Urologic Sciences, University of British Columbia, 2775 Laurel Street, 6th Floor, Vancouver, BC, V5Z 1M9, Canada. ryan.flannigan@ubc.ca.

Funding

American Urological Association Foundation Rising StarMichael Smith Health Research BC Health Professional Investigator Grant
6 · The paper itself

Abstract

purpose of reviewInfertility affects approximately 15% of couples globally, with male-factor infertility contributing to about half of these cases. Despite advancements in reproductive medicine, particularly in surgical methods, the prevalence of male infertility remains high and underreported, often due to cultural stigmas. Traditional semen analysis, a crucial component in diagnosing male infertility, involves subjective assessments, leading to variability in results. This review explores the advancements and applications of Artificial Intelligence (AI) in diagnosing and treating male infertility, emphasizing its potential to revolutionize the field by providing reliable and efficient diagnostic tools and improving treatment outcomes. RECENT

findingsRecent advances in reproductive medicine, including techniques like microdissection testicular sperm extraction and intracytoplasmic sperm injection, have improved conception rates. However, the integration of AI in andrology offers even greater promise. AI techniques, including machine learning and artificial neural networks, now provide automated and objective analysis of sperm motility, and DNA integrity, significantly improving diagnostic precision. These technologies outperform traditional methods by reducing subjectivity in sperm evaluation, identifying subtle abnormalities often missed during manual assessments, and enhancing the selection process for assisted reproductive technologies. Moreover, AI-based predictive models optimize patient selection and personalize treatment protocols, increasing success rates. AI-driven technologies hold transformative potential in the field of reproductive medicine by enhancing the accuracy and efficiency of diagnosing and treating male infertility. The automated and objective analysis offered by AI can offer the possibilities of achieving parenthood for infertile men. However, the implementation of these technologies must be carefully managed, with particular attention to ethical considerations such as bias, transparency, and data privacy. AI's role in advancing reproductive medicine is promising, but responsible deployment is essential to maximize its benefits.

Indexed as

AndrologyArtificial IntelligenceInfertility, MaleHumansMaleSemen Analysis

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.