Infertility affects approximately one in six couples globally, a condition recognized by the World Health Organization (WHO) as one of the most serious global disabilities. As the demand for solutions grows, assisted reproductive technology (ART), including in vitro fertilization (IVF), has emerged as a critical option for those facing infertility challenges.
However, the IVF process generates vast amounts of data, and each patient requires a highly personalized treatment approach. Given the complexity and variability of IVF protocols, clinicians often face difficulties in reviewing and applying all relevant data when designing individualized treatment plans. This is where Explainable Artificial Intelligence (XAI) can make a significant difference.
XAI technology is capable of managing and processing large, intricate datasets, enhancing the ability of clinicians to access and utilize critical information. By improving how data is analyzed and applied, XAI can optimize personalized ART treatments, ensuring that no essential data is overlooked and improving patient outcomes. This advancement has the potential to transform the way IVF treatments are administered, leading to more effective and tailored fertility care.
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