Artificial Intelligence for Women and Child Healthcare: Is AI Able to Change the Beginning of a New Story? A Perspective

Abstract

Background and Aims: Maternal and neonatal mortality remain critical global health challenges, particularly in low-resource settings where preventable deaths occur due to inadequate access to timely care. This article explores the potential of Artificial Intelligence (AI) to enhance maternal and child healthcare by improving early risk identification, diagnosis, treatment recommendations, and postpartum monitoring. Methods: It explores the use of AI in identifying pregnancy-related risks, recommending treatments, predicting adverse outcomes, and monitoring postpartum and neonatal care. Various AI models, including supervised machine learning, Large Language Models (LLMs), and Small/Medium Language Models (SLMs/MLMs), are discussed in terms of their feasibility into resource-limited healthcare systems. Results: AI has demonstrated significant potential in identifying pregnancy-related risks, recommending treatments, predicting adverse outcomes, and supporting postpartum and neonatal care. While AI-driven solutions can optimize healthcare decision-making and resource allocation, challenges such as data availability, integration into clinical workflows, and ethical considerations must be addressed for widespread adoption. Conclusion: AI offers promising solutions to reduce maternal and neonatal mortality by enhancing risk detection and clinical decision-making. However, its real-world implementation requires overcoming barriers related to data quality, infrastructure, and equitable deployment. Future efforts should focus on data standardization, AI model optimization for resource-limited settings, and ethical considerations in clinical integration.

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