Smart Healthcare Infrastructure For Early Detection of Neonatal Mortality Risks Using AI And Iot - Poornima, Galiveeti; Arockia Mary, P.; Saikia, Bhabajyoti; Haidari, Moazzam; P, Latha; Moharana, Ankita (Journal of Neonatal Surgery, 2025)
Neonatal mortality remains a significant global health challenge, particularly in low-resource settings. Early detection of risk factors such as infections, prematurity, and asphyxia can improve neonatal survival rates. This paper presents a smart healthcare infrastructure aimed at the early detection of neonatal mortality risks through the integration of Artificial Intelligence (AI) and the Internet of Things (IoT). The proposed system utilizes IoT devices such as wearable sensors to continuously monitor critical neonatal health parameters, including heart rate, oxygen levels, and temperature. AI models are employed to analyze the data, identifying abnormal patterns indicative of potential risks. The integration of real-time alerts ensures prompt intervention by healthcare professionals, thereby improving outcomes. The results show promising accuracy in both sensor data collection and AI-driven risk predictions. Challenges such as data privacy, model accuracy, and system integration are discussed, alongside strategies for overcoming these barriers. This smart infrastructure aims to reduce neonatal mortality by enabling early, data-driven decision-making in neonatal care.