Artificial Intelligence and Deep Learning for Sustainable Health Systems in Jordan: A Policy and Implementation Framework

Abstract

Abstract The role of artificial intelligence (AI) and deep learning (DL) in enhancing health systems is becoming more actively studied, especially in the areas where the delivery of services is uneven and the data systems are decentralized. These problems are reflected in maternal and child morbidity, nutrition, and regional differences in care access in Jordan. The article develops a theoretical and policy-oriented model of AI implementation in the Jordanian national health care.

According to the results of the Jordan Population and Family Health Survey (2023) and the most recent literature, the study aims at four areas, which include mortality risk prediction, maternal and perinatal health, nutrition monitoring, and disability-sensitive spatial planning. It evaluates the right AI strategies, data requirements and implementation feasibility.

As stated in the discussion, AI can enhance the early identification of risks, intervention targeting, and planning reinforcement. However, it needs to be integrated with data, institutional preparedness, governance, and ethical concerns to be successful in its adoption. In order to apply responsibility and context-sensitive implementation, a roadmap is proposed in phases.