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Foundation models: the next level of AI in ART.

Hugo L Hammer1, Vajira Thambawita2, Michael A Riegler3

  • 1Mathematical Modelling, Oslo Metropolitan University, Oslo, Norway.

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|July 15, 2025
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Summary

Foundation models offer advanced artificial intelligence (AI) for assisted reproductive technology (ART). These AI tools enhance embryo selection, sperm diagnostics, and personalized treatment by integrating diverse clinical data.

Keywords:
artificial intelligenceembryo technologyfertilityfoundation modelhuman reproductionmachine learningsemen quality

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Area of Science:

  • Reproductive Medicine
  • Artificial Intelligence
  • Biotechnology

Background:

  • Traditional artificial intelligence (AI) in assisted reproductive technology (ART) uses narrow models for specific tasks like embryo selection.
  • These models require extensive manual annotation and do not integrate diverse clinical data effectively.
  • Foundation models, pre-trained on large datasets, offer robust multimodal analysis and decision support.

Purpose of the Study:

  • Discuss the technical aspects of foundation models in AI.
  • Explore the applications of foundation models in ART.
  • Highlight innovations in AI-driven ART for improved outcomes.

Main Methods:

  • Review of foundation model technology and self-supervised learning.
  • Analysis of current AI applications in ART, including embryo selection and sperm analysis.
  • Integration of recent advancements in AI for ART.

Main Results:

  • Foundation models show promise for robust multimodal analysis in ART.
  • AI-driven methods can enhance embryo selection and sperm epigenetics diagnostics.
  • Personalized treatment protocols can be developed using AI integration.

Conclusions:

  • Foundation models represent a significant advancement for AI in ART.
  • These models can improve efficiency and effectiveness in clinical reproductive practices.
  • Addressing challenges like data quality and regulation is crucial for widespread adoption.