The Role of Artificial Intelligence in Predicting Pregnancy Complications
DOI:
https://doi.org/10.63682/jns.v14i33S.9893Keywords:
Machine Learning, Deep Learning, Explainable AI, Preeclampsia, High-Risk Pregnancies, Preterm BirthAbstract
Artificial Intelligence (AI) has emerged as a transformative tool in maternal healthcare, offering predictive capabilities to assess and manage pregnancy complications. This review examines the role of AI-driven techniques, including machine learning (ML), deep learning (DL), and hybrid models, in improving risk prediction for conditions such as gestational diabetes, preeclampsia, preterm birth, and recurrent pregnancy loss. A systematic analysis of recent literature highlights the strengths of AI-based models in analyzing maternal health data, ultrasound images, and wearable sensor readings to enable early detection and personalized risk assessment. However, challenges such as data quality, model interpretability, algorithmic bias, and integration into clinical workflows remain significant barriers to widespread adoption. AI-powered predictive frameworks integrated with Internet of Things (IoT) technology show potential for real-time maternal health monitoring, enhancing preventive care and timely interventions. Furthermore, AI applications in assisted reproductive technologies (ART) improve embryo selection and in vitro fertilization (IVF) outcomes, although ethical concerns and regulatory compliance require further exploration. This review underscores the need for explainable AI (XAI) approaches, data standardization, and ethical oversight to ensure reliable and equitable maternal healthcare solutions. Future research should focus on enhancing AI transparency, addressing dataset biases, and developing clinically viable AI systems to optimize pregnancy risk prediction and maternal-fetal health outcomes..
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