AI-enabled Smart Hospital Respiratory Monitoring and Early Warning Device for Continuous Assessment of Respiratory Rate, Oxygen Saturation, Pulse Rate and Respiratory Distress with Automated Nursing Alerts

Respiratory deterioration is a major concern among hospitalized patients and may progress rapidly if early warning signs are not recognized and appropriate interventions are delayed. Traditional hospital monitoring generally depends on periodic manual assessment of respiratory rate, oxygen saturation and pulse rate, which may fail to identify sudden physiological changes between observations. Artificial intelligence (AI), wearable sensors, wireless communication and automated clinical alert systems offer new opportunities for continuous patient monitoring and early identification of respiratory deterioration. This review discusses the development and potential application of an AI-enabled smart hospital respiratory monitoring and early warning device designed to continuously assess respiratory rate, peripheral oxygen saturation (SpO₂), pulse rate and indicators of respiratory distress. The proposed system integrates non-invasive physiological sensors, intelligent data processing, patient-specific risk analysis and automated nursing alerts. Continuous monitoring may improve the recognition of abnormal respiratory patterns, identify changes in physiological parameters and support timely nursing assessment and clinical intervention. The review also examines the role of AI algorithms, wireless connectivity, electronic health records, clinical decision-support systems and nursing responsibilities in the implementation of smart respiratory monitoring. Important considerations include sensor accuracy, false alarms, patient privacy, data security, clinical validation and integration with existing hospital systems. Although available studies demonstrate promising technological capabilities, further large-scale clinical research is necessary to establish improvements in patient outcomes. AI-enabled respiratory monitoring should therefore be considered a supportive clinical technology that strengthens, rather than replaces, professional nursing assessment.