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Related Concept Videos

Infertility in Males01:23

Infertility in Males

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Male infertility affects millions of couples worldwide, arising from various factors that impact different stages of the reproductive process. An endocrine imbalance resulting from conditions like hypogonadism, Klinefelter syndrome, or pituitary disorders can disrupt hormone levels and reduce sperm production. Testicular defects, such as tumors, cryptorchidism, atrophic testes, abnormal sperm morphology, and low sperm count or motility, may arise due to genetic factors, structural...
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Female infertility is defined as the inability to conceive after a year of regular, unprotected intercourse and affects about 10–15% of couples worldwide. The primary cause of female infertility is ovulatory disorders, which hinder the release of eggs. These disorders can be classified as hypothalamic amenorrhea, polycystic ovarian syndrome (PCOS), premature ovarian failure, and hyperprolactinemic anovulation disorders.
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Reproductive cloning is the process of producing a genetically identical copy—a clone—of an entire organism. While clones can be produced by splitting an early embryo—similar to what happens naturally with identical twins—cloning of adult animals is usually done by a process called somatic cell nuclear transfer (SCNT).
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The reproductive system generates offspring, ensuring the survival of the species. In humans, the reproductive system is complex and involves a variety of organs and hormones that work together to ensure successful reproduction.
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AI in Reproductive Medicine: Hope or Hype?

Shruti Agarwal1, Alexander M Quaas2, Mark P Trolice1

  • 1University of Central Florida-HCA Consortium of Greater Orlando.

Clinical Obstetrics and Gynecology
|December 22, 2025
PubMed
Summary

Artificial intelligence (AI) enhances assisted reproduction by improving ovarian stimulation, embryo assessment, and predicting IVF success. Further validation is needed to address concerns about generalizability and equity in reproductive endocrinology.

Keywords:
IVFartificial intelligenceembryo selectionendometriumethicsovarian stimulationsperm

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Area of Science:

  • Reproductive medicine
  • Medical artificial intelligence
  • Infertility research

Background:

  • Artificial intelligence (AI) is increasingly integral to reproductive endocrinology and infertility (REI) clinical practice.
  • AI applications span ovarian stimulation, gamete/embryo assessment, endometrial evaluation, and IVF outcome prediction.
  • These technologies aim to reduce subjectivity and standardize assessments in assisted reproduction.

Purpose of the Study:

  • To review the current applications of AI in assisted reproduction.
  • To highlight the potential benefits of AI in improving objectivity and providing prognostic information.
  • To identify the limitations and future research needs for AI in REI.

Main Methods:

  • Review of reported AI applications in reproductive endocrinology and infertility.
  • Analysis of AI's role in various stages of assisted reproductive technology (ART).
  • Evaluation of predictive models for fertilization, implantation, miscarriage, and live birth rates.

Main Results:

  • AI tools are being developed for ovarian stimulation, gamete/embryo assessment, and predicting IVF outcomes.
  • Predictive models and decision-support tools for embryo transfer and treatment continuation exist.
  • Most current studies are retrospective and focus on intermediate outcomes, not definitive clinical results.

Conclusions:

  • AI offers potential benefits for reducing subjectivity and standardizing care in REI.
  • Significant concerns regarding generalizability, transparency, accountability, and equity persist.
  • Rigorous, outcome-based validation is essential to confirm AI's true clinical value in assisted reproduction.