ROLE OF ARTIFICIAL INTELLIGENCE IN RISK PREDICTION OF PRETERM LABOR: NARRATIVE REVIEW

Abstract

Objective: This study explores the role of Artificial Intelligence (AI) in predicting preterm labor, highlighting its potential to improve maternal and fetal health outcomes. It examines AI’s effectiveness, challenges, and future directions, emphasizing the integration of advanced predictive models and ethical considerations in healthcare.
Method: A comprehensive review of literature was conducted to assess AI-based approaches, including machine learning algorithms such as random forests, support vector machines (SVM), and neural networks. Key challenges, such as ethical concerns, data bias, and the integration of AI into clinical workflows, were analyzed alongside opportunities for leveraging telemedicine, wearable technologies, and collaborative research efforts.
Findings: AI models have demonstrated remarkable accuracy in predicting medical outcomes, offering personalized risk assessments and enabling early interventions for preterm labor. Deep learning techniques excel in complex pattern recognition, while SVM and random forests show robust performance in risk prediction tasks. However, challenges such as data privacy, lack of regulatory frameworks, and limited generalizability of AI models hinder widespread adoption. Future opportunities lie in integrating AI with telemedicine and wearable devices to enhance access to care, particularly in underserved regions.
Conclusion: AI-driven technologies hold immense promise for transforming maternal healthcare, particularly in preterm labor prediction. Addressing ethical and infrastructural challenges through multidisciplinary collaboration and targeted initiatives is critical. By leveraging its potential, AI can redefine healthcare delivery, bridging gaps in access and enabling precision obstetrics.