Artificial Intelligence Applications in Pneumonia: Diagnosis and Outcome Prediction

Abstract Purpose of Review This review explores the current use of artificial intelligence (AI) in pneumonia diagnosis and outcome prediction. It aims to highlight advancements in AI technologies, focusing on both imaging and electronic health record-based approaches, their impact on improving diagnostic accuracy, and predicting clinical outcomes. Recent Findings AI systems can both diagnose pneumonia and predict disease severity, mortality, and other key outcomes, such as hospital length of stay and readmission risk. These tools integrate diverse data sources, including demographics, lab markers, and vital signs, to enhance clinical decision-making. Recent imaging models using neural networks demonstrated high accuracy in detecting pneumonia from chest X-rays and CT scans, surpassing human radiologists in some cases. However, challenges remain, including inconsistencies in pneumonia labeling, data quality issues, and the limited generalizability of models across different healthcare settings. Summary AI holds significant potential to improve pneumonia diagnosis and patient outcomes, though challenges such as data biases, model interpretability, and standardization remain. Continued research is needed to address these limitations and optimize AI integration into clinical practice.