The prospect of artificial intelligence to personalize assisted reproductive technology

Simon Hanassab, Ali Abbara, Arthur C. Yeung, Margaritis Voliotis, Krasimira Tsaneva-Atanasova, Tom Kelsey, Geoffrey H. Trew, Scott M. Nelson, Thomas Heinis, Waljit S. Dhillo*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


Infertility affects 1-in-6 couples, with repeated intensive cycles of assisted reproductive technology (ART) required by many to achieve a desired live birth. In ART, typically, clinicians and laboratory staff consider patient characteristics, previous treatment responses, and ongoing monitoring to determine treatment decisions. However, the reproducibility, weighting, and interpretation of these characteristics are contentious, and highly operator-dependent, resulting in considerable reliance on clinical experience. Artificial intelligence (AI) is ideally suited to handle, process, and analyze large, dynamic, temporal datasets with multiple intermediary outcomes that are generated during an ART cycle. Here, we review how AI has demonstrated potential for optimization and personalization of key steps in a reproducible manner, including: drug selection and dosing, cycle monitoring, induction of oocyte maturation, and selection of the most competent gametes and embryos, to improve the overall efficacy and safety of ART.
Original languageEnglish
Article number55
Number of pages19
Journalnpj Digital Medicine
Issue number55
Publication statusPublished - 1 Mar 2024


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