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Related Concept Videos

In Vitro Fertilization01:24

In Vitro Fertilization

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In vitro fertilization (IVF) is a form of assisted reproductive technology where an egg is fertilized with sperm in a controlled laboratory environment before transferring the resulting embryo into the uterus. This process is designed to help individuals and couples experiencing difficulties conceiving.
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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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Updated: Jan 13, 2026

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Artificial Intelligence in Routine IVF Practice.

Grzegorz Mrugacz1, Aleksandra Mospinek2, Małgorzata Jagielska3

  • 1Center for Reproductive Medicine Bocian, Białystok Branch, 26 Akademicka St., 15-267 Białystok, Poland.

Biology
|January 10, 2026
PubMed
Summary

Artificial Intelligence (AI) enhances in vitro fertilization (IVF) by improving embryo selection and reducing embryologist workload. While AI shows promise in predicting embryo viability, further research is needed to confirm its impact on live birth rates.

Keywords:
IVFartificial intelligenceclinical validationembryo selectionethicsinteroperability

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Area of Science:

  • Reproductive Medicine
  • Biotechnology
  • Medical Informatics

Background:

  • Artificial Intelligence (AI) is revolutionizing in vitro fertilization (IVF) by addressing limitations of traditional embryo assessment, such as subjectivity and labor intensity.
  • AI leverages deep learning and computer vision for advancements in embryo selection, treatment personalization, and outcome prediction.
  • Platforms like ERICA, iDAScore, and IVY exemplify AI's application in overcoming traditional IVF assessment challenges.

Purpose of the Study:

  • To synthesize evidence on AI's role in IVF, evaluating its evolution, clinical validation, and ethical considerations.
  • To provide a roadmap for responsible AI adoption in reproductive medicine.
  • To focus on AI's integration, real-world outcomes, and future policy recommendations.

Main Methods:

  • Review of current evidence on AI in IVF.
  • Analysis of AI's impact on embryo selection, personalization, and prediction.
  • Evaluation of clinical validation, ethical challenges, and integration pathways.

Main Results:

  • AI models demonstrate 10-25% higher accuracy in predicting embryo viability and implantation potential compared to morphological assessment.
  • AI adoption can reduce embryologist workload by 30-50% per embryo cohort.
  • Hybrid AI-embryologist decision-making models offer objective, data-driven support, standardizing workflows and reducing subjectivity.

Conclusions:

  • AI significantly enhances the precision and efficiency of IVF, particularly in embryo ranking and supporting elective single-embryo transfer (eSET).
  • While AI shows promising trends, large-scale trials are needed to confirm significant increases in live birth rates.
  • Sustainable AI integration requires robust evidence, interdisciplinary collaboration, global standardization, and ethical vigilance to maintain patient trust and equity.