Artificial Intelligence and Smart Oxygen Delivery Systems In Respiratory Care: Emerging Trends, Clinical Applications, and Future Directions

Abstract

Background: Respiratory disorders remain among the leading causes of morbidity and mortality worldwide, placing a substantial burden on healthcare systems and affecting millions of individuals annually. Oxygen therapy is a cornerstone of respiratory care and is commonly used in the management of acute and chronic respiratory conditions, including chronic obstructive pulmonary disease (COPD), pneumonia, acute respiratory distress syndrome (ARDS), interstitial lung diseases, asthma exacerbations, and respiratory failure. Despite its widespread use, conventional oxygen therapy often relies on manual adjustments based on intermittent monitoring of oxygen saturation and clinical assessment. Such approaches may lead to periods of hypoxemia or hyperoxia, both of which are associated with adverse patient outcomes. Recent advances in artificial intelligence (AI), machine learning (ML), sensor technology, wearable devices, and smart medical systems have opened new opportunities for optimizing oxygen delivery and improving respiratory care outcomes. Objective: This review aims to examine emerging trends in AI-driven oxygen therapy and smart oxygen delivery systems, evaluate their clinical applications in respiratory care, assess their benefits and limitations, and identify future directions for research and implementation in clinical and nursing practice. Methods: A narrative review methodology was adopted to synthesize current evidence on AI-assisted oxygen therapy and smart oxygen delivery technologies. Relevant literature published in peer-reviewed journals, conference proceedings, healthcare technology reports, and respiratory care publications was reviewed. Studies focusing on AI-based oxygen regulation, closed-loop oxygen delivery systems, predictive analytics, remote respiratory monitoring, smart oxygen concentrators, wearable biosensors, and digital respiratory care platforms were included. Evidence was analysed thematically and categorized into technological advancements, clinical applications, effectiveness, challenges, limitations, and future opportunities. Results: The review identified significant advancements in AI-enabled oxygen therapy systems. A total of 52 studies meeting the eligibility criteria were included in the final review. Closed-loop oxygen delivery devices demonstrated the ability to automatically adjust oxygen flow rates based on real-time physiological measurements, reducing episodes of hypoxemia and hyperoxia. Machine learning algorithms showed promise in predicting oxygen requirements and identifying early signs of respiratory deterioration. Smart oxygen concentrators integrated with cloud-based monitoring platforms improved home oxygen therapy management and facilitated remote patient supervision. Wearable sensors enabled continuous monitoring of oxygen saturation, respiratory rate, heart rate, and activity levels, generating valuable data for AI-driven decision making. Clinical applications were particularly evident in COPD, ARDS, COVID-19-related respiratory failure, sleep-disordered breathing, and long-term oxygen therapy settings. AI-assisted respiratory care was associated with improved oxygen titration accuracy, enhanced patient safety, reduced clinician workload, and greater personalization of treatment. However, several challenges remain. These include concerns related to algorithm transparency, data privacy, cybersecurity, healthcare inequalities, implementation costs, regulatory approval, and limited evidence from large-scale randomized controlled trials. Ethical considerations regarding autonomous decision-making and accountability also require careful attention. Conclusion: Artificial intelligence and smart oxygen delivery systems are transforming respiratory care by enabling precise, adaptive, and personalized oxygen therapy. Emerging technologies have the potential to improve patient outcomes, optimize healthcare resource utilization, and enhance the quality of respiratory management across acute and chronic care settings. Future research should focus on validating AI algorithms through robust clinical trials, addressing ethical and regulatory concerns, and developing accessible technologies suitable for diverse healthcare environments. The integration of AI into oxygen therapy represents a promising step toward intelligent respiratory care and precision medicine.