Artificial intelligence for arterial blood gas interpretation.

Arterial blood gas (ABG) analysis is a fundamental diagnostic tool in clinical medicine, offering critical insights into a patient's respiratory and metabolic status. However, interpreting ABG results can be complex and time-sensitive, necessitating accurate and rapid analysis. With the advancement of artificial intelligence (AI), new avenues have emerged to enhance the interpretation and application of ABG data. This review explores the role of AI in ABG analysis, highlighting how machine learning algorithms and natural language models such as ChatGPT can aid in the systematic interpretation of complex physiological data. We examine the mechanisms through which AI systems analyze ABG parameters, including pH, PaCO₂, and HCO₃-, and provide diagnostic recommendations. Specific applications, such as AI-driven models, in the detection of COVID-19 severity and pulmonary hypertension via ABG data are discussed, demonstrating the expanding clinical utility of AI technologies. Additionally, we explore the potential of 3D animated computer models as educational and diagnostic tools for interpreting blood gas data. The integration of AI into ABG interpretation holds promise for improving diagnostic accuracy, clinical decision-making, and patient outcomes, signaling a transformative shift in modern healthcare diagnostics.