Peer-reviewed article
Future directions in infertility research: the role of generative AI and large language models.
Hasan M Jamil ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This paper discusses how artificial intelligence and large language models could be used to advance research on male infertility and reproductive health. It explores potential applications of AI in analyzing genetic data, assessing sperm quality, and predicting health risks in offspring. The paper is written for researchers and discusses technical and ethical considerations of using AI in fertility science.
Key points
- Male factor infertility accounts for up to 50% of infertility cases globally, with many cases still unexplained
- Large language models could help synthesize large volumes of research literature and generate new research hypotheses
- AI could assist in interpreting complex genomic and epigenetic data related to male infertility
- Polygenic risk scoring and computational simulations could potentially predict long-term health effects from paternal factors
- Responsible use of AI requires attention to data quality, interpretability, and ethical considerations
Limitations
This is a conceptual chapter about future research directions rather than a study evaluating actual outcomes for children. It does not present empirical evidence about the impact of any intervention on child well-being.
Abstract
Male infertility remains an understudied yet significant contributor to global reproductive health challenges, with up to 50% of infertility cases involving a male factor and a large proportion still classified as idiopathic. Recent advances in generative artificial intelligence (AI), particularly large language models (LLMs), offer a transformative opportunity to tackle persistent gaps in understanding the genetic, epigenetic, and environmental determinants of male infertility. This chapter explores the scientific potential of LLMs and related AI technologies in accelerating discov-eries across the male reproductive research continuum - from interpreting complex genomic data and identifying novel gene - environment interactions to enhancing sperm quality assessment and predicting an unborn child's long-term health risks stemming from paternal factors. Real-world examples and emerging case studies illustrate how generative AI can help fertility researchers learn rapidly, synthesize massive volumes of literature, generate hypotheses, design experiments, and reveal patterns that conventional analyses may miss. The narrative further reflects on the implications of using AI to forecast offspring health via polygenic risk scoring and in silico developmental simulations, highlighting both technical promise and ethical considerations. Written from the perspective of a computational scientist collaborating with fertility experts, this chapter demonstrates how interdisciplinary approaches, amplified by LLMs, can lower barriers between computer science and reproductive biology. By embracing generative AI responsibly - with attention to data quality, interpretability, and social responsibility - male infertility researchers stand poised to unlock novel insights that will benefit not only current patients but also future generations.