Human Fertility

With the current development of artificial intelligence (AI), the intersection of technology and biology allows a new level of scientific inquiry and innovation, particularly in the realm of reproductive health. "AI in Fertility, IVF, and Reproductive Medicine" is an ambitious Collection focusing on this intersection, where advanced computational models meet intricate biological processes. This Collection aims to highlight the transformative role of AI, especially foundation models, in revolutionizing reproductive health. By leveraging AI's unparalleled analytical capabilities, the Collection seeks to uncover novel insights and methodologies that can significantly enhance the accuracy of reproductive health research and diagnostics.

The integration of AI into reproductive health is not just a technological advancement, but it is a pivotal step towards more personalized, accurate, and comprehensive reproductive healthcare. Traditional methods of analysis in this context are often limited by their subjective nature and the inherent complexity of biological variability. AI-driven approaches promise to overcome these limitations by providing more objective, consistent, and detailed analyses. This is crucial for improving fertility treatments, understanding reproductive health better, and advancing scientific knowledge in the field. Additionally, by aligning these technological advancements with a focus on ethical and trustworthy practices, the Collection ensures that this cutting-edge research remains responsible and beneficial for all stakeholders involved.

This Collection invites contributions that explore and expand the frontiers of AI for reproductive health. We welcome research articles that demonstrate state-of-the-art AI methods, including but not limited to machine learning, deep learning, and predictive analytics applied to different aspects of reproductive health ranging from semen analysis over embryo analysis to outcome prediction. Emphasis is also placed on contributions that address ethical alignment and trustworthiness in the AI applications within this context. Furthermore, the Collection is open to dataset papers that describe novel, open datasets instrumental in building AI applications. Through these contributions, the Collection aims to build a comprehensive understanding of how AI can be effectively and responsibly integrated into reproductive health. The scope includes, but is not limited to, AI-driven diagnostic tools, predictive models for fertility assessment, automated image analysis for evaluation of semen and embryos, and the development of ethical guidelines for AI in reproductive health research. By bringing together a diverse range of articles, this Collection will serve as a cornerstone for researchers and practitioners looking to explore the convergence of AI and reproductive biology.

Michael A. Riegler is a chief research scientist at SimulaMet and a professor at OsloMet. His research focuses on AI in different medical and biological application areas such as reproductive health.

Disclosure Statement: Prof. Riegler declares no conflict of interest regarding this work.  

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