Artificial Intelligence in Reproductive Medicine: A Survey on Infertility Prediction and Diagnosis

Abstract

Infertility is a multifactorial and rising global health issue that is complicated by intricate
interplay of hormonal imbalance, metabolic dysfunction, genetic predisposition,
structural anomalies and lifestyle factors. Conventional methods of diagnosis, which rely
mostly on single-test clinical methods, like hormonal profiling, imaging, and semen
analysis tend to miss non-linear and interdependent interactions underlying reproductive
dysfunction. The recent developments in the field of Artificial Intelligence (AI) and
specifically the field of Machine Learning (ML) and Deep Learning (DL) have brought to
the table potent data-driven approaches, which can be utilized to integrate the
heterogeneous medical data, and reveal latent patterns to predict infertility early and
accurately. The paper provides a thorough review of AI-based infertility prediction
methods that include classical machine learning, deep learning architectures, and the new
multimodal and hybrid models. The paper examines a wide range of data such as clinical
records, hormonal biomarkers, medical imaging (ultrasound and MRI), genetic data and
lifestyle factors. Special attention is paid to disorder-specific applications including
Polycystic Ovary Syndrome (PCOS), endometriosis, male infertility and assisted
reproductive technologies (ART). Also, the utilization of Explainable Artificial
Intelligence (XAI) methods like SHAP and LIME is discussed to improve model
transparency and clinical trust. Although there have been tremendous improvements, one
can still blame the heterogeneity of data, scarcity of data, class imbalance, non-
standardization, ethical issues with privacy and bias as major obstacles to clinical
implementation. The future directions described in the paper are multimodal data fusion,
federated learning, interpretable AI systems and personalized reproductive medicine.
Altogether, AI-based infertility prediction tools have a great potential to revolutionize the
reproductive healthcare as they allow diagnosing patients at an early stage, enhance
treatment planning, and facilitate precision medicine.